Regression discontinuity design. J Econ 142:615–635.

Regression discontinuity design Many empirical applications of RD designs involve continuous treatments. We discuss simila-rities and differences between these packages and provide directions on how to Chapter 13 Regression Discontinuity Design. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, This course covers methods for the analysis and interpretation of the Regression Discontinuity (RD) design, a non-experimental strategy to study treatment effects that can be used when units receive a treatment based on a score and a cutoff. Find examples, assumptions, versions, and advice for choosing and using this method. National Bureau of Economics working papers, Cambridge. Regression discontinuity designs in economics. Regression Discontinuity Design Introduction and Practical Advice Liam Rose Health Economics Resource Center, VA Palo Alto. 1 and Figures 4. These continuity assumptions may not be plausible if Regression Discontinuity Design. 7 17 Exploiting a There are several types of Regression Discontinuity: Sharp RD: Change in treatment probability at the cutoff point is 1. I The RDD has proven to be very useful when treatment is This study introduces a doubly robust (DR) estimator for regression discontinuity (RD) designs. admission into treatment is based on a score denoted s I with scores above 100, say, leading to treatment (d = 1). Its basic characteristic is that the treatment is assigned based on Keywords: st0366 1, rdrobust, rdbwselect, rdplot, regression discontinuity 1 Introduction The regression-discontinuity (RD) design is widely used in applied work. 2,8,9 In recent years, a number of clinical and population health studies have been published in economics journals using regression discontinuity We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. INTRODUCTION Regression discontinuity (RD) design is “one of the most credible non-experimental strategies for the analysis of causal effects” (Cattaneo, Idrobo, and Titiunik,2020a, page. A regression discontinuity design is analyzed as follows: the outcome variable (Y, self-efficacy) is regressed on the treatment variable (X, attending the test prep class or not) and the assignment variable (Z, tenth grade test score). Kink design: Instead of a discontinuity in the level of running variable, we have a discontinuity in the slope of the function (while the function/level can remain continuous) (Nielsen, Sørensen, and Taber 2010). There are different techniques for creating a valid comparison group such as regression discontinuity design (RDD) and propensity score matching (PSM). This study examines the impact of air pollution on exports in China using a regression discontinuity design based on the Qinling Mountains-Huai River line. This paper establishes Regression Discontinuity Design(RDD) is another widely used method to make causal inference which is consider as more reliable and more robust. Econometrica) Learn how to use regression discontinuity design (RDD) to measure the impact of an intervention based on a continuous eligibility index. Regression discontinuity designs (RDDs or RDs) are a quasi-experimental design. 3). We use RD when passing a certain threshold induces a change in the RHS variable of interest. 5 found in Mastering Metrics (based on data from Carpenter and Dobkin 2009) Will adding controls affect diff-in-diff estimates if treatment assignment was random? In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolás Idrobo, and Rocıìo Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and Assumptions for the regression discontinuity design. In Section 4, I generalize the model to a setting with multiple time periods. One thus obtains the following regression equation: Y The results shed light on the re-understanding of China's family planning initiative as well as the application of regression discontinuity designs. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but Regression Discontinuity Design (RDD) RCT: random assignment to treatment and control •Groups are balanced in expectation on all variables, including unobserved ones RCT gold standard for estimating causal effects of treatments •Observational studies: often hard to justify that treatment groups are comparable, including on unobservables. individuals, –rms, governments, economies etc. Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, inference, and However, this design may not be appropriate in every setting, so other methods or designs such as the regression discontinuity design (RDD) are required. , bias correction (Calonico et al. In this second monograph, we discuss several topics in RD Regression discontinuity design (RDD) is a quasi-experimental method intended for causal inference in observational settings. The goal of this paper is to estimate the effect of statins on myocardial infarction (MI) The regression discontinuity design (RDD) is a valuable tool for identifying causal effects with observational data. I’ve never estimated one of these, and am not an expert, but thought it might be 1 Introduction. 13. Do developmental mathematics programs have a causal impact on student retention: An application of discrete-time survival and regression discontinuity analysis. The regression discontinuity (RD) design is one of the most widely used nonexperimental methods for causal inference and program evaluation. It introduces the most recent developments and advanced methods and provides the key intuitions that underlie the statistical Despite this, few published studies have used the regression discontinuity design to evaluate clinical questions (table 1 ⇑). Acyclic Graphs Case-Control Study Regression Discontinuity Design Rubin, Don Causal Estimation and Causal Inference Causality Causal Analysis With Regression discontinuity (RD) analysis is a rigorous nonexperimental1 approach that can be 6 Collinearity Coefficient and Sample Size Multiple for a Regression Discontinuity Design Relative to an Otherwise Comparable Randomized Trial, by the [1], the regression discontinuity (RD) design has recently become one of the most widely used quasi-experimental strategies. Nowadays, the design is well known and widely used in a variety of disciplines, including (but Regression Discontinuity Designs with a Continuous Treatment Yingying Dong, Ying-Ying Lee, Michael Gou First version: April 2017; this version: April 2021 Abstract The standard regression discontinuity (RD) design deals with a binary treatment. This guide covers graphical presentation, • Regression Discontinuity Design (RDD). It presents basic and intuitive insights into the concepts and theory underlying the research design and presents results from an existing empirical study to enhance understanding of the key elements of regression discontinuity designs. RD designs are also easy to We discuss the two most popular frameworks for identification, estimation and inference in regression discontinuity (RD) designs: the continuitybased framework, where the conditional expectations of the potential outcomes are assumed to be continuous functions of the score at the cutoff, and the local randomization framework, where the treatment assignment is Regression Discontinuity Design Let's Give It a Try to Evaluate Medical and Public Health Interventions Jan P. 1 Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO-Piemonte, Turin, Italy; 2 MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, United Kingdom; 3 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; Regression discontinuity design (RDD) Regression discontinuity (RD) analysis is a rigorous nonexperimental1 approach that can be 6 Collinearity Coefficient and Sample Size Multiple for a Regression Discontinuity Design Relative to an Otherwise Comparable Randomized Trial, by the Local regression of some kind is how most researchers choose to implement their regression discontinuity design, at least if they have a large sample. Regression discontinuity designs: A guide to practice. Published in volume 48, issue 2, pages 281-355 of Journal of Economic Literature, June 2010, Abstract: This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical resear Regression Discontinuity Design (RDD) in Brief This method was developed to estimate treatment e⁄ects in non-experimental settings. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their advantages and Regression discontinuity design (RDD) has proven to be a compelling and transparent research design to estimate treatment effects. However, clinical researchers are likely to be less familiar with and have less training in quasi-experimental designs. Once we control for a confounder X i, treatment assignment is as good as random. By comparing observati This paper reviews some of the practical and theoretical issues in implementing regression discontinuity (RD) methods for evaluating causal effects of intervention A comprehensive introduction and guide to RD designs, a quasi-experimental method for estimating treatment effects in nonexperimental settings. 2004. These designs were first introduced in the evaluation literature by Thistlewaite and Campbell [1960. Cunningham documents a rapid increase in the number of papers using RDD after 1999 and attributes its popularity to its ability to deliver causal inferences with “identifying assumptions that are viewed by many as easier to accept and evaluate” than Regression discontinuity designs (RD designs or RDD) have been widely used in empirical social science research in recent years. Nowadays, the design is well known and widely used in a variety of disciplines, including (but The Regression Discontinuity (RD) design has emerged as one of the most credible research designs in the social, behavioral, biomedical, and statistical sciences for program evalua-tion and causal inference in the absence of an experimentally assigned treatment. JEL classi cation: C14, C31. In such scenarios, the Regression Discontinuity Design (RDD) method comes into play. cn March 12, 2024 Abstract Treatment effects in regression discontinuity designs (RDDs) are often estimated using local regression methods. This can be a natural cutoff such as a geographical border, or an intervention like a grade requirement for qualifying scholarship. Here, we showcase recent applications, we developa protocol for the regression discontinuity design. advocatedforinterpretingtheRDdesignasinducing“asgoodasrandomized”(p. In this Regression-Discontinuity Design with Ten-point Treatment Effect. The idea is that observations just below and just above the threshold are fairly comparable. Cattaneo† Rocio Titiunik‡ January 11, 2024 The regression discontinuity (RD) design is a research strategy employed to study the causal effect of a treatment (e. 3 Selection on Observables: Matching, Regression and Propensity Score Estimators; 4 Selection on Unobservables: Non-Parametric IV and Structural Equation Approaches; 5 Difference-in-Differences Estimation: Selection on Observables and Unobservables; 6 Regression Discontinuity Design; 7 Distributional Policy Analysis and Quantile Treatment Effects Regression discontinuity designs identify a local average treatment effect: the average effect of treatment exactly at the cutoff. Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. Yet, for somewhat unclear reasons—and despite the extensive efforts that Donald Campbell’s research team at Northwestern University put into studying it—this research design was relatively underutilized for several decades after Interpreting Regression Discontinuity Designs with Multiple Cutoffs Matias D. 5 % 50 0 obj /Filter /FlateDecode /Length 761 >> stream xÚuUKsÚ0 ¾çWø(fÀ•l ¦·¶¤ t¦= nM B XScyôHÊ¿ïJ+ˆIè íK»Ÿv?/4;d4ûvGÿs~ÞÞ}øZÓŒÑ|EW,Ûî³¢fùjµÊ–´Îi5϶Mö‹|ÚYg¸p“ßÛïï’@†q‚ [Ty¹ÌfE —éþC?™ eE\«l æ侓GÙOŠš8tñ¾A r6Y„ÐÇ CÐ’œT?aäðîî -?¸S][çhû ã½ÔS4ÿTBwO´('sJ O0 The regression discontinuity (RD) design was introduced by Thistlethwaite and Campbell (1960) more than 50 years ago, but has gained immense pop-ularity in the last decade. 1. Cattaneo , Nicolás Idrobo , Rocío Titiunik Regression Discontinuity Design Introduction and Practical Advice Liam Rose Health Economics Resource Center, VA Palo Alto. In this lab session we will: Leverage visualizations with ggplot2 to explore our discontinuity setups; Learn how to model our discontinuity setups under different functional forms with lm() Regression discontinuity designs (RDDs) are an underused methodology in healthcare research that can overcome the limitations of traditional improvement science designs . , intervention or policy) on an outcome of interest using observational Although not required for the validity of the design, in most cases, the reason for the discontinuity in the probability of the treatment does not suggest a discontinuity in the average value of Learn how to use regression discontinuity design (RDD), a quasi-experimental method that compares outcomes of individuals just above and below a treatment threshold, to How Has Regression Discontinuity Design Been Used in Crime and Justice Research? By Jonathan Nakamoto, Alexis Grant, Trent Baskerville, Anthony Petrosino. For identification we propose a Regression Discontinuity Design implied by an increase in the minimum school leaving age in 1947 (from One of the potential advantages of the regression discontinuity design is that of visual transparency – many results can be clearly seen graphically before needing to do any estimation, as illustrated by Dave Evans in this post. Over the last twenty years or so, the regression discontinuity design (RDD) has seen a dramatic rise in popularity among researchers. 1 However, some randomised Hierarchical Regression Discontinuity Design: Pursuing Subgroup Treatment Effects Shonosuke Sugasawa1∗, Takuya Ishihara2 and Daisuke Kurisu3 1Faculty of Economics, Keio University 2Graduate School of Economics and Management, Tohoku University 3Center for Spatial Information Science, The University of Tokyo ∗Corresponding (Email: The Regression Discontinuity (RD) design has emerged as one of the most credible research designs in the social, behavioral, biomedical, and statistical sciences for program evaluation and causal inference in the absence of an experimentally Regression discontinuity designs (RD designs or RDD) have been widely used in empirical social science research in recent years. Two important reasons for its appeal are that the research design permits clear and transparent identification of causal parameters of interest, This monograph, together with its accompanying first part Cattaneo, Idrobo and Titiunik (2020), collects and expands the instructional materials we prepared for more than $50$ short courses and workshops on Regression Discontinuity (RD) methodology that we taught between 2014 and 2023. Quantity-quality trade-off. 1 Introduction Regression discontinuity designs, seeThistlethwaite and Campbell(1960), are widely rec-ognized as one of the most credible quasi-experimental strategies for identifying and for which the treatment effect is adjusted in the analysis. Introduction Examples from literature Polynomials Should not be Used in Regression Discontinuity Designs" \We argue that estimators for causal e ects based on [higher order polynomials] can be misleading, Regression discontinuity design in economics. 2008. Today REGRESSION DISCONTINUITY DESIGNS USING COVARIATES Sebastian Calonico, Matias D. Regression discontinuity design (RDD) is a method for evaluating scenarios where intervention is determined by the certain cutoff value (e. I thought I’d share some of The regression discontinuity design (RDD) is a valuable tool for identifying causal effects with observational data. Journal of Economic Literature 48(2): 281–355 Manuscript submitted to The Econometrics Journal , pp. What I want is a function that will split the running variable by a given binwidth and then create a binned scatterplot. It presents the basic theory behind the research design, details when For each framework, we discuss three main topics: (a) designs and parameters, focusing on different types of RD settings and treatment effects of interest; (b) estimation and inference, Regression discontinuity design is a statistical method used to estimate causal effects in observational studies. S. Method: The aim of this article is to introduce the RDD, summarise methodology in the context of health services research and present a worked example using the statistic software SPSS (Examples for R and Stata in the Regression Discontinuity Design measures the treatment effect at a cutoff, thus we can only apply RDD if there is a clear cutoff that separates the treatment and control group. 2014. , intervention or policy) on an outcome of interest using observational data. Regression Discontinuity Designs in Economics by David S. In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretest–posttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned. I withheld from units whose score is less than the cuto . . 2 The Sharp Regression Discontinuity Design It is useful to distinguish between two general settings, the Sharp and the Fuzzy Re-gression Discontinuity (SRD and FRD from hereon) designs (e. The Regression Discontinuity Design* Matias D. There Abstract. , 89–90) to estimate the causal effect of a treatment on various outcomes of interest. Regression Discontinuity Design (RDD) is a quasi-experimental impact evaluation method used to evaluate programs that have a cutoff point determining who is eligible to participate. Am Econ J Econ Policy 5(4):29–77. We performed simulations and a validation study in which we used treatment effect estimates from an RCT as the reference for a prospectively performed regression discontinuity study. In a nutshell, such designs exploit the discontinuity in the treatment assignment probability around the cutoff value of some running variable. In this design, which we call the Geographic Regression Discontinuity (GRD) design, a geographic or administrative boundary splits units into treated and control areas, and analysts make the case that the division into treated and control areas Regression Discontinuity Design (RDD) In the problem set we are going to practice RDD in the Lee (2008) framework. misassignment have been suggested (Trochim, 1984), mis-assignment is better avoided if possible. 2 Regression discontinuity designs have wide application in a variety of elds Under appropriate assumptions, they allow causal inferences in situations where they seem very counterintuitive Rather than being damaged by selection, the design capitalizes on it Multilevel Regression Discontinuity Designs. The regression discontinuity (RD) design was introduced by Thistlethwaite and Campbell (1960) more than 50 years ago, but has gained immense pop-ularity in the last decade. Causal Inference and Regression Discontinuity Design Regression discontinuity designs, see Thistlethwaite and Campbell (1960), are widely recognized as one of the most credible quasi-experimental strategies for identifying and estimating causal effects. Regression Discontinuity Design(RDD) is another widely used method to make causal inference which is consider as more reliable and more robust. Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs 1 Sebastian Calonico y, Matias D. Zhaopeng Qu ( NJU ) Lec6 Regresssion Discontinuity November 17 2021 5/99. edu James Orin Mur n Associate Professor Department of Political Science Caroline Flammer Regression Discontinuity Design 31 Literature • Econometrics of RDD: Imbens GW, Lemieux T. In Section 5, I consider alternative approaches to aggregate time-specific parameters. The RDD method leverages arbitrary cutoffs, such as the distinct difference in letter grades between a B and an A, resulting from a small score change (e. , 2001, HTV from hereon), adaptive estimation methods (Sun, 2005), specific methods for choosing bandwidths (Ludwig and Miller, 2005), and various tests for discontinuities in means and distributions of non-affected Standard sufficient conditions for identification in the regression discontinuity design are continuity of the conditional expectation of counterfactual outcomes in the running variable. Lee DS, Lemieux T. 604 604 The flexibility and focus on the cutoff is the upside, but (as with nearly any time you toss out data or drop assumptions), by imposing less structure on the data you get less precision in your estimates. Article Google Scholar Imbens GW, Lemieux T (2008) Regression discontinuity designs: a guide to practice. Causal Inference and Regression Discontinuity Design Regression Discontinuity Design Idea: Find an arbitrary cutpoint c which determines the treatment assignment such that Ti = 1fXi cg Close elections as RD design (Lee et al. I The original idea was to exploit policy thresholds to estimate the causal e ect of an educational intervention. It covers graphical analyses, estimation Regression discontinuity designs are non-randomized study designs that permit strong causal inference with relatively weak assumptions. Farrell yy y Department of Health Policy and Management, Columbia University, USA. e. This document replicates the Table 4. The web page explains the sharp RD regression, the conditional independence Use the asymptotic approximation to bias and variance of local linear regression estimator at the boundary Refinements, e. Our regression discontinuity design necessitates a sample of children with ages around the policy cutoff of 5 years (60 months). Imbens GW, Lemieux T (2008) Regression discontinuity designs: a guide to practice. An extension of this approach which is growing in usage is the regression kink design(RKD). 3 %. Over the last two decades, statistical and econometric This course covers methods for the analysis and interpretation of the Regression Discontinuity (RD) design, a non-experimental strategy to study treatment effects that can be used when units receive a treatment based on a score and a cutoff. When RDD is perfectly implemented, We discuss the two most popular frameworks for identification, estimation and inference in regression discontinuity (RD) designs: the continuitybased framework, where the conditional expectations of the potential outcomes are assumed to be continuous functions of the score at the cutoff, and the local randomization framework, where the treatment assignment is Class-Size Caps, Sorting, and the Regression-Discontinuity Design by Miguel Urquiola and Eric Verhoogen. One issue which comes up is then how to do power calculations for these studies. Regression discontinuity (RD) designs have become increasingly popular in political science, due to their ability to showcase causal effects under weak assumptions. Zhu and Weiwei Jiang Institute of Science and Technology for Brain-inspired Intelligence Fudan University, Shangha China E-mail: rongzhu@fudan. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, Becker SO, Egger PH, von Ehrlich M (2013) Absorptive capacity and the growth effects of regional transfers: a regression discontinuity design with heterogeneous treatment effects. RDD allows researchers to compare the people immediately above and below the cutoff point to identify the impact of the program on a given outcome. Regression discontinuity designs have wide application in a variety of elds Under appropriate assumptions, they allow causal inferences in situations where they seem very counterintuitive Rather than being damaged by selection, the design capitalizes on it The Regression Discontinuity (RD) design has emerged in the last decades as one of the most credible non-experimental research strategies to study causal treatment e ects. Acyclic Graphs Case-Control Study Regression Discontinuity Design Rubin, Don Causal Estimation and Causal Inference Causality Causal Analysis With This paper examines the influence of housing wealth on fertility outcomes through a regression discontinuity design based on a 2006 Chinese housing-market policy. Regression discontinuity (RD) analysis is a rigorous nonexperimental1 approach that can be 6 Collinearity Coefficient and Sample Size Multiple for a Regression Discontinuity Design Relative to an Otherwise Comparable Randomized Trial, by the The regression discontinuity (RD) design is one of the most widely used nonexperimental methods for causal inference and program evaluation. Unique to the RD design is that The regression-discontinuity (RD) research design assigns participants to treatment groups solely on the basis of a pretreat-ment cutoff score, allowing the relative effect of treatment to be Learn how to implement regression discontinuity (RD) analysis, a nonexperimental approach to estimate program impacts based on a cut-point. It is one of the most credible quasi-experimental research designs for identification, estimation, and inference of treatment effects (local to the cutoff). Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, Regression discontinuity (RD) designs have gained signicant popularity as a quasi-experimental device for evaluating education programs and policies. Let’s have a look at some examples. In the SRD design REGRESSION DISCONTINUITY DESIGN STANDARDS CERTIFICATION. Replication files and illustration codes employing these packages are also available. Individuals who wish to become a WWC RDD certified reviewer will need to view all eleven modules, pass a multiple-choice certification test, and then complete an example study review guide. Previous article in issue; Next article in issue; Keywords. This brief examines Think of encouragement designs or imperfect compliance (like the Oregon study) 9/51. Regression discontinuity (RD) models, which can be traced back to Thistlethwaite and Campbell (), are popular in policy evaluations or other settings of treatment effect analysis. Arai et al. The Analysis of the Regression-Discontinuity Design in R Felix Thoemmes Wang Liao Ze Jin Cornell University This article describes the analysis of regression-discontinuity designs (RDDs) using the R packages rdd, rdrobust, and rddtools. A simple RDD estimate compares the average value of outcome Regression Discontinuity Designs in Economics by David S. Cattaneo, Nicolas Idrobo, and Rocio Titiunik provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. Passage of such “close-call” proposals akin to random assignment of long-term incentives to companies → provides clean causal estimate. Regression discontinuity designs (Thistlethwaite and Campbell (), Hahn, Todd, and Van der Klaauw ()) can deliver credible identification of treatment effects from observational data in settings where the probability of receiving the treatment changes discontinuously with a running variable at some known threshold value. This article focuses on Keywords: Regression Discontinuity Designs, Manipulation, Diagnostic Tests. In the absence of treatment the assumption is that the pre–post We study a special type of regression discontinuity design where the discontinuity in treatment assignment is geographic. Cattaneo Roc o Titiunik Gonzalo Vazquez-Bare§ June 1, 2020 Handbook chapter published in Handbook of Research Methods in Political Science and International Relations Another way to use institutional features is the use of a Regression Discontinuity (RD) design. In this paper we detail the entire Regression Discontinuity Design (RDD) history, including its origins in the 1960s, and its two main waves of formalization in the 1970s and 2000s, both of which are rarely acknowledged in the literature. ) are ‚treated™based on a known cuto⁄ rule. The regression discontinuity design first appeared in the educational psychology literature in 1960, 3–5 was further developed in the 1970s and 1980s, 6,7 and has become well established in economics over the last 2 decades. Purpose Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. A common approach in RD estimation is to apply nonparametric regression methods, Exploiting a regression discontinuity design, this paper tests whether the adoption of a development approach to refugee assistance in a new settlement in Kenya has a positive impact. g. Continuity Assumption. Today • Fundamentals • How to interpret Regression Discontinuity Designs in Economics David S. Interest in these designs is growing but there is Learn how to conduct and interpret regression discontinuity design (RDD), a quasi-experimental method to estimate causal effects from observational data. Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, inference, and Regression Discontinuity Design Units receive a score (X i). The findings show that air pollution harms firm exports, with a more pronounced impact on non-state-owned enterprises, heavily polluting industries, and densely populated regions. (2007). In this design, units receive treatment based on whether their value of an observed covariate or “score” is above or below a This study introduces a doubly robust (DR) estimator for regression discontinuity (RD) designs. Lee and Thomas Lemieux. 4 4. regression discontinuity (FRD) designs allowing for general heterogeneity of treatment effects (Hahn et al. Econ): be a continuous and smooth function of vote shares everywhere, except at the threshold that determines party membership. (2022) show that if the parameter of interest is the LATE or the local quantile treatment effects, then the continuity of the running variable density and the continuity of the predetermined variable distributions are neither sufficient nor necessary (also This study investigates the identification and inference of quantile treatment effects (QTEs) in a fuzzy regression discontinuity (RD) design under rank similarity. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their advantages and 按照在断点处个体得到处理效应概率的变化特征可以分为两种类型:一种类型是精确断点回归设计 (sharp regression discontinuity design, 以下简称SRD) , 和模糊断点回归设计 (fuzzy regression discontinuity, 以下简称FRD) 。 Downloadable! The Regression Discontinuity (RD) design is one of the most widely used non-experimental methods for causal inference and program evaluation. This article establishes identification and robust bias-corrected inference for such RD designs. A search of PubMed and the Cochrane Library (performed on May 11, 2017) for the term regression discontinuity yielded only four studies that prospectively applied a healthcare The regression discontinuity design: Methods and implementation with a worked example in health services research June 2022 Zeitschrift für Evidenz Fortbildung und Qualität im Gesundheitswesen This article provides an introduction to an estimation strategy called ‘regression discontinuity’. This paper formalizes the difference-in-discontinuities design, a hybrid approach that combines the regression-discontinuity design and the difference-in-difference design. Regression discontinuity design. Since its invention by Thistlethwaite and Campbell (1960), the regression discontinuity design (RDD) has attracted much attention among econometricians; see Imbens and Lemieux (2008), van der Klaauw (2008) and Lee and Lemieux (2010) for excellent reviews on up-to-date theoretical developments and applications and Yu (2013) for a summary of treatment We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. Cattaneo, Max H. J. While this design is widely employed to mitigate the effects of confounding treatments co-occurring at discontinuities, few studies provide complete presentations of identifying assumptions and 1 Introduction. Published in volume 48, issue 2, pages 281-355 of Journal of Economic Literature, June 2010, Abstract: This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical resear In Regression Discontinuity (RD) designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a fixed threshold. Our analysis reveals that the positive impact of this policy on housing wealth significantly enhances the likelihood of fertility by 7. Keywords: st0366 1, rdrobust, rdbwselect, rdplot, regression discontinuity 1 Introduction The regression-discontinuity (RD) design is widely used in applied work. Review of RDD. Therefore, to ensure sufficient representation, we use ten waves of data from the continuous cycles of the public-use National Health and Nutrition Examination Survey Keywords: regression discontinuity designs, shape constraints, monotonicity, isotonic regression, boundary point, wild bootstrap. There are two distinct features of RDD. The method requires observing a variable and a cutoff point that either The regression discontinuity design (RDD) was initially introduced by Thistlewaite and Campbell to examine the influence of merit awards on students’ the future academic This paper reviews the practical and theoretical issues in implementing regression discontinuity designs for evaluating causal effects of interventions. In other words, in the absence of the treatment, the outcome would follow a smooth, continuous function across the cutoff (note that regression discontinuity design using the predicted synthetic score as the running variable to estimate the treatment effect on an outcome of interest. Over the last two decades, statistical and econometric In Regression Discontinuity (RD) designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a fixed threshold. Longevity is modelled by survival analysis using a mixed proportional hazard model. Sekhon Political Analysis , Volume 19 , Issue 4 We investigate the long-run impact of education on longevity using data for England and Wales from the Health and Lifestyle Survey. calonico@columbia. Unlike Frandsen et al. Learn the theory, assumptions, In regression discontinuity (RD) designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an Learn how to use the regression discontinuity design (RD) to estimate causal effects of a treatment. We estimated the treatment effect using linear regression adjusting for the assignment variable both as linear terms and restricted The Analysis of the Regression-Discontinuity Design in R Felix Thoemmes Wang Liao Ze Jin Cornell University This article describes the analysis of regression-discontinuity designs (RDDs) using the R packages rdd, rdrobust, and rddtools. The regression discontinuity (RD) research design is a quasi-experimental design that can be used to assess the effects of a treatment or intervention. The main trouble with the design is that there is vanishingly little data exactly at the cutoff, so any answer strategy needs Quasi-experimental designs identify a comparison group that is as similar as possible to the treatment group in terms of baseline (pre-intervention) characteristics. This article provides a checklist, These slides give an introductory example of regression discontinuity design (RDD) I RDD is a method for causal inference I it can be applied when treatment occurs when a variable that The regression discontinuity (RD) design is a research strategy employed to study the causal effect of a treatment (e. You Class-Size Caps, Sorting, and the Regression-Discontinuity Design by Miguel Urquiola and Eric Verhoogen. The treatment is: I given to units whose score is greater than the cuto . This paper provides an intuition-based guide for the use of the RD in applied research. It is applied specifically in situations where individuals are assigned to a policy/intervention based on whether they are above or below a pre-specified cut-off on a Regression-Discontinuity Designs Rong J. On the other hand, many RDD designs, especially those with fitted polynomials, don’t always look so clear, and have been the subject The regression discontinuity design (RDD) occurs when assignment to treatment depends deterministically on a quantified score on some continuous assignment variable. These designs are applicable when a continuous “scoring” rule is used to assign the intervention to study units (for example, school Regression Discontinuity Design 153. Two important reasons for its appeal are that the research design permits clear and transparent identification of causal parameters of interest, Regression Discontinuity Design with Vector-Argument Assignment Rules∗ Guido Imbens† Tristan Zajonc‡ April 27, 2009 Abstract Regression discontinuity designs identify causal effects by Downloadable! This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. Cattaneo, University of Michigan Luke Keele, Penn State University Rocío Titiunik, University of Michigan Gonzalo Vazquez-Bare, University of Michigan We consider a regression discontinuity (RD) design where the treatment is received if a score is above a cutoff, but the Regression Discontinuity designs have become a popular addition to the impact evaluation toolkit, and offer a visually appealing way of demonstrating the impact of a program around a cutoff. The regression-discontinuity design was proposed by Thistlethwaite and Campbell (1960) and discussed extensively by Trochim (1984). In RD designs, the treatment assignment is based on a running variable that The regression discontinuity design: Methods and implementation with a worked example in health services research June 2022 Zeitschrift für Evidenz Fortbildung und Qualität im Gesundheitswesen The second strand focuses on testing the identifying assumptions of LATE-type parameters in FRD designs. In this Element and its accompanying Element, Matias D. At 6-months postbaseline, both groups had improved. The basic idea of Regression Discontinuity Design (RDD) is the following: Observations / subjects (e. Background: The regression discontinuity design (RDD) is a quasi-experimental approach used to avoid confounding bias in the assessment of new policies and interventions. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their Keywords: Regression discontinuity; local experiment; as-if random assignment; local randomization The regression discontinuity (RD) design is a research strategy based on three main components a score or “running variable,” a cutoff, and a treatment. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, Regression discontinuity design is a statistical method used to estimate causal effects in observational studies. Farrell, and Rocío Titiunik* Abstract—We study regression discontinuity designs when covariates are included in the estimation. Cattaneo† Roc o Titiunik‡ February 25, 2022 Abstract The Regression Discontinuity (RD) design is one of the most widely used non-experimental methods for causal inference and program evaluation. Key words and phrases: pre-registration plans, observational studies, causal inference, regressiondiscontinuity designs. Regression Discontinuity Design Regression Discontinuity Design A threshold variable (denoted s) determines treatment status I e. A search of PubMed and the Cochrane Library (performed on May 11, 2017) for the term regression discontinuity yielded only four studies that prospectively applied a healthcare Hierarchical Regression Discontinuity Design: Pursuing Subgroup Treatment Effects Shonosuke Sugasawa1∗, Takuya Ishihara2 and Daisuke Kurisu3 1Faculty of Economics, Keio University 2Graduate School of Economics and Management, Tohoku University 3Center for Spatial Information Science, The University of Tokyo ∗Corresponding (Email: Experimental Designs Randomization Make a selection: Campbell, Donald Pearl, Judea Popper, Karl Raimund Goldthorpe, John Cartwright, Nancy Hedges, Larry Randomization Blalock, Hubert Lazarsfeld, Paul F. These designs were first introduced in the evaluation literature by Thistlewaite and Campbell (1960). The validity of a Sharp RD design relies on the continuity assumption, which means that the two potential outcomes are expected to be continuous at the threshold. We discuss simila-rities and differences between these packages and provide directions on how to Software packages for analysis and interpretation of regression discontinuity designs and related methods. , Trochim, 1984, 2001; HTV). We begin by introducing key concepts, assumptions, and estimands within both the continuity-based framework and the local randomization framework. However, applying the traditional electoral RDD to the study of divided government is challenging. Elections and the Regression Discontinuity Design: Lessons from Close U. With the right setup, the estimates are causal. the treatment group) have been raised by 10 points on the posttest. Figure 2 is identical to Figure 1 except that all points to the left of the cutoff (i. This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. 1 Quick Review of diff-in-diff. The regression discontinuity design: Methods and implementation Exploiting a regression discontinuity design, this paper tests whether the adoption of a development approach to refugee assistance in a new settlement in Kenya has a positive impact. J Econ 142:615–635. The Regression Discontinuity (RD) design is one of the most widely used non-experimental methods for causal inference and program evaluation. I was wondering if SAS had any automated feature for creating a Regression Discontinuity plot. 7 14 15 16 In the example in figure 1 ⇑, Bor et al show the power of the regression discontinuity approach by examining the role of early antiretroviral treatment on mortality, retention, and immune recovery in South Africa. RD designs are also easy to Regression Discontinuity Design Introduction and Practical Advice Liam Rose Health Economics Resource Center, VA Palo Alto. Overview. Regression Discontinuity Design Jörn-Ste⁄en Pischke LSE October 26, 2018 Pischke (LSE) RD October 26, 2018 1 / 15. edu period regression discontinuity design and illustrate the core of my identification strategy. A common approach in RD estimation is to apply nonparametric regression methods, 2. Multiple Cutoff Points RD designs are not limited to a single cutoff value. This score is then used as a covariate in a regression of outcome. Over the This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. edu. This article uses the example of statin prescription in primary care to explain the concept of the method and how it can be used Randomised controlled trials are, in most scenarios, the best scientific method for evaluating the efficacy of treatment. In this paper, we present a comprehensive review of RD designs, focusing on the continuity-based framework, the Regression Discontinuity Designs∗ Matias D. Regression discontinuity designs (RDD) are increasingly being employed in agricultural and environmental economics to identify causal effects. Such designs are called sharp (SRD) if the probability 1 Introduction. 1{20. The method requires observing a variable and a cutoff point that either ultimately determines treatment assignment or is a strong predictor of treatment. Keywords: Regression Discontinuity Designs, Manipulation, Diagnostic Tests. This work was supported in part by the National Science Regression discontinuity designs use observational data to examine treatment efficacy. In RD designs, treatment effects are estimated in a quasi-experimental setting where treatment assignment depends on whether a running variable surpasses a predefined cutoff. The distinctive feature behind the RD design is that all units receive a score, and a treatment 2. The set-up exploits discontinuity in the design of many policies to nonparametrically identify treatment effects for observations near the eligibility cutoff. In this guide for practitioners, we discuss several features of this regression discontinuity in time framework A regression discontinuity design was used to assess optimal strategies for matching need to service intensity. Sekhon sekhon@berkeley. Our CSs are based on local linear regression, and are bias-aware, in the sense that they take possible bias explicitly into account. Regression discontinuity design is a statistical method used to estimate causal effects in observational studies. A. In this Element, The Regression Discontinuity (RD) design has emerged as one of the most credible research designs in the social, behavioral, biomedical, and statistical sciences for program evaluation and causal inference in the absence of an experimentally assigned treatment. The distinctive feature behind the RD design is that all units receive a score, and a treatment 1 Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO-Piemonte, Turin, Italy; 2 MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, United Kingdom; 3 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; Regression discontinuity design (RDD) Regression discontinuity is an appropriate design to study this issues as young adults are “naturally” selected in two groups based on their age: young adults who are below the age of 21 are not legally allowed to drink while young adults above the age of 21 are allowed to drink. If a candidate obtains more votes than his or her competitors, he or she takes the office. While RDD is gaining popularity in clinical studies, there are limited real-world studies examining the performance on estimating known trial casual effects. This chapter reviews the main assumptions and key challenges faced when adopting an RDD. Introduction. In Section 3, I extend the two-period model to fuzzy designs. RD is based on the seemingly paradoxical idea that rigid rules create valuable experiments. We establish conditions under which the method identifies the local treatment effect for a unit at Regression discontinuity (RD) designs are increasingly used by researchers to obtain unbiased estimates of the effects of education-related interventions. A treatment is assigned based on the score and a known cuto (c). Under assumptions, the abrupt change in the probability of treatment The regression discontinuity (RD) design is one of the most widely used nonexperimental methods for causal inference and program evaluation. This article focuses on Regression Discontinuity Designs in Economics David S. Published in volume 99, issue 1, pages 179-215 of American Economic Review, March 2009, Abstract: This paper examines how schools' choices of class size and households' choices of schools affect Provides a convenient wrapper function for data analysis with regression discontinuity design (especially discrete running variables) as an identification strategy. For the outlined regression discontinuity design to identify the direct and spillover effects, individuals must be unable to precisely control the running variable, date of birth, near the discontinuity point, which permits a near-random variation in treatment in the neighborhood of the In regression discontinuity (RD) designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a fixed threshold. (2012, Journal of Econometrics 168, 382–395), who focus on QTEs only for the compliant subpopulation, our approach can identify QTEs and average treatment effect for the whole population at the Software packages for analysis and interpretation of regression discontinuity designs and related methods. Cattaneo z and Max H. 2 4. This work was supported in part by the National Science Cambridge University Press 978-1-108-71020-6 — A Practical Introduction to Regression Discontinuity Designs Matias D. Today I haven’t done a lot of RD evaluations before, but recently have been involved in two studies which use regression discontinuity designs. 2010. House Races, 1942–2008 Devin Caughey , Jasjeet S. We find that refugees benefiting from the new approach have better diets and perceive themselves as happier and more independent from humanitarian aid. When \(a\) is our running variable, first, treatment status is deterministic of \(a\) and second, Treatment status is a discontinuous function of \(a\). Cattaneo Roc o Titiunik Gonzalo Vazquez-Bare§ June 1, 2020 Handbook chapter published in Handbook of Research Methods in Political Science and International Relations The regression discontinuity design is a statistical approach that utilizes threshold based decision making to estimate causal estimates of different interventions Regression discontinuity is relatively simple to implement, transparent, and provides “real world” effects of treatments and policies However, this design may not be appropriate in every setting, so other methods or designs such as the regression discontinuity design (RDD) are required. Journal of Econometrics 142(2): 615–635. Google Scholar Lesik, S. In the SRD design As randomized controlled trials are not always feasible, quasi-experimental methods, such as regression discontinuity design, can expand the scope of clinical investigations aimed at causal inference in observational settings. Experimental Designs Randomization Make a selection: Campbell, Donald Pearl, Judea Popper, Karl Raimund Goldthorpe, John Cartwright, Nancy Hedges, Larry Randomization Blalock, Hubert Lazarsfeld, Paul F. 675)variation intreatmentstatusnearthecutoff,aswellastheworkofLee&Lemieux(2010). r econometrics causal-inference discrete-variables regression There are several types of Regression Discontinuity: Sharp RD: Change in treatment probability at the cutoff point is 1. Regression Discontinuity Designs in Economics David S. Lee and Thomas Lemieux* This paper provides an introduction and “user guide” to Regression Discontinuity (RD) designs for empirical researchers. , threshold) of a continuous variable. Research in Regression Discontinuity Designs∗ Matias D. Article MATH MathSciNet Google Scholar Imbens GW, Zajonc T (2009) Regression discontinuity design with vector-argument assignment rules unpublished paper During this week's lecture you were introduced to Regression Discontinuity Designs (RDDs). %PDF-1. Published in volume 99, issue 1, pages 179-215 of American Economic Review, March 2009, Abstract: This paper examines how schools' choices of class size and households' choices of schools affect Regression Discontinuity Design (RDD) is a quasi-experimental research method that leverages a naturally occurring discontinuity or threshold to estimate the causal effect of a treatment or The standard regression discontinuity (RD) design deals with a binary treatment. Vandenbrouckea and Saskia le Cessiea,b The regression discontinuity design has been described as the next best thing after a randomized trial: it may produce valid causal inferences, at least under some assump Foundations of regression discontinuity - the fuzzy design. B. We employ the original simplified data set on the individual candidates for the US House of Representatives from 1946 to 1998. The bins should be counted from the point of discontinuity so that each bin contains either all treatment or all control observations. Understanding Regression Discontinuity Designs As Observational Studies Jasjeet S. In RD designs, the treatment assignment is based on a running variable that Regression discontinuity designs (RDDs) are an underused methodology in healthcare research that can overcome the limitations of traditional improvement science designs . Sharp RD Regression: conditional independence assumption E[Y 0ijX i,D i] = E[Y 0ijX i]. Today I The Regression Discontinuity Design (RDD) was rst introduced in the econometrics literature during the 1960s[5]. INTRODUCTION A protocol,sometimesreferred to asa“pre-registration plan”, is a detailed outline of a research study that speci- As randomized controlled trials are not always feasible, quasi-experimental methods, such as regression discontinuity design, can expand the scope of clinical investigations aimed at causal inference in observational settings. E-mail: sebastian. edu Robson Professor Departments of Political Science and Statistics UC-Berkeley 210 Barrows Hall #1950, Berkeley, CA 94720-1950 Roc o Titiunik titiunik@umich. Overview Regression discontinuity designs (RDDs or RDs) are a quasi-experimental design. One-child policy. Q.