interpreting regression discontinuity results

Lee and Lemieux (2010), Imbens and Lemieux (2007), and Cook (2008) provide comprehensive reviews of regression discontinuity design and its applications in the social sciences. 2015), and the connections to multi-scoresandgeographicRDdesigns (KeeleandTitiunik 1230 / Interpreting Regression Discontinuity Designs Matias D. Cattaneo et al. 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. PDF Regression Discontinuity Designs Using Covariates . 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. A regression discontinuity design (RDD, for short) arises when the selection of a unit into a treatment group depends on a covariate score that creates some discontinuity in the probability of receiving the treatment. PDF Regression Discontinuity Designs in Economics Cattaneo, Keele, Titiunik and Vazquez-Bare (2016): Interpreting Regression Discontinuity Designs with Multiple Cutoffs. Regression discontinuity analysis is often a disaster. So ... developments in the analysis and interpretation of regression discontinuity (RD) designs. The regression-discontinuity (RD) design is widely used in applied work. LectureNote 19 — CausalInference Usingthe Regression DiscontinuityDesign David Autor,Massachusetts Institute of Technology TheRegression DiscontinuityDesign During thein-class componentof thislecture/topic, we will study the2010 paperon securitization Regression Discontinuity Design 1 Motivating example • Many districts have summer school to help kids improve outcomes between grades - Enrichment, or - Assist those lagging • Research question: does summer school improve outcomes:se•Vblaira - x=1 is summer school after grade g - y = test score in grade g+1 2 • Equation of interest . ‐regression results are often presented in tables ‐however, displaying results graphically can be much more effective: easier to see and remember patterns and trends female age bmi region==NE region==MW region==S.8 1 1.2 1.4 1.6 1.8 odds ratio: diabetes diabetes female 1.066 (0.102) age 1.059*** (0.004) . By comparing observations lying closely on either side of the threshold, it . Regression Discontinuity (RD) Regression discontinuity design is useful for estimating the causal effect of an explanatory variable in the case where there is an observable jump or discontinuity in the level of the explanatory variable. Regression Discontinuity Design (RDD) is a quasi-experimental evaluation option that measures the impact of an intervention, or treatment, by applying a treatment assignment mechanism based on a continuous eligibility index which is a variable with a continuous distribution. ars report results and balance tests from regression-discontinuity designs. Within the causal inference framework, this design is considered among the most credible non-experimental strategies because it relies on relatively weak and easy- example: Jacob and Lefgren (2004), T indicates going to summer school. These results are Y is an outcome, e.g. X is a running or forcing variable that a ects T and may also a ect Y, e.g, X is a nal exam grade. effect of politician winning one election on chance of winning next election. Again, this estimate is very close to the true 10-point treatment effect. Can't just compare treated and nontreated units. A Practical Introduction to Regression Discontinuity Designs. Regression Discontinuity Practical Applications Causal Inference There are 5 basic empirical methods to obtain causal inference: 1 Controls (includes matching/ﬁxed-eﬀects) 2 Randomized Experiments 3 Diﬀerence-in-Diﬀerences 4 Instrumental Variables 5 Regression Discontinuity Regression discontinuity design, a quasi-experimental approach, mimics randomized experimental designs, allowing for causal interpretation of observed effects within observational data , , , , . Figure 1.5: Treatment effect. Regression discontinuity (RD) is an econometric method that can be used to deduce the causal e ect of a treatment that is determined by a mechanical rule. 1 Thistlethwaite and Campbell (1960) first proposed the regression discontinuity design based on the insight that, given an eligibility rule based on a cutoff value for a . Abstract: The Regression Discontinuity Design (RDD) has proven to be a compelling and transparent research design to estimate treatment effects. Compared to the randomized experiment (RE), the regression discontinuity design (RDD) has three main limitations: (1) In expectation, its results are unbiased only at the treatment cutoff and not for the entire study population; (2) it is less efficient than the RE and so requires more cases for the same statistical power; and (3) it requires correctly specifying the functional form that . It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, a regression discontinuity design, builds a solid foundation for making causal inferences. Interpreting results for mlogit: Ordered Logit Regression: ologit v201 daughter_son_ratio v133 v012 poorest poorer middle richer: In an ordered logit model the actual values taken on by the categorical dependent variable are irrelevant, except that larger values are assumed to correspond to 'higher' outcomes. 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. Regression results for initial model without quadratic terms. Within the causal inference framework, the RD design is considered to be one of the most credible nonexperimental strategies be-cause it relies on weak and easy-to-interpret nonparametric Second, my analysis provides strong causal evidence that bank branch expansion led to positive e ects on agricultural productivity, manu-facturing outcomes and local income growth. Regression Discontinuity Design (RDD) is a quasi-experimental evaluation option that measures the impact of an intervention, or treatment, by applying a treatment assignment mechanism based on a continuous eligibility index which is a variable with a continuous distribution. It does so by accounting for the relationship between the outcome and the rating variable and then calculating the difference between the two groups. The output is as follows: . Regression discontinuity. Spatial regression discontinuity • Spatial regression discontinuity is a special case that recognizes geographic borders as sharp cutoff points. It is considered one of the most robust non-experimental evaluation designs when it is feasible to implement. See Cook (2008) for a historical perspective. The third is to modify and optimize the regression discontinuity design based on basics, such as regression discontinuity designs with assignment variable (Papay et al., 2011;Rao et al., 2019 . How to do regression discontinuity and interpret results in Stata 16 Oct 2017, 18:10 . Note that the most recent advances in the RDD literature suggest that it is not very accurate to interpret a discontinuity design as a local experiment. Among other things, it established the need to provide falsiﬁcation tests . X is a running or forcing variable that a ects T and may also a ect Y, e.g, X is a nal exam grade. Introduction • Causal for us so far: selection of observables, instrumental variables for when this doesn't hold • Basic idea behind both: find some plausibly exogeneous variation in the treatment assignment • Selection on observables: treatment as-if random conditional on • IV: instrument provides exogeneous variation • Regression Discontinuity: exogeneous variation from a Caroline Flammer Regression Discontinuity Design 4 • Leaving aside controlled experiments, three main methods of causal inference: 1) IV (instrumental variables) 2) DID (difference-in-differences) 3) RDD (regression discontinuity design) • 1) and 2) increasingly popular in strategy research. Last edited by Andrew Kenny; 16 Oct 2017, 18:11 . Test Results Interpreting the result from a Hausman test is fairly . based on cuto s of expected response. The interpretation of RD designs as local experiments developed by Lee (2008) has been very in uential, both conceptually and practically. The cut-off is based on a continuous rather than categorical variable. Our results are nonparametric, easy to compute, and control for the endogeneity induced by the targeting rule. regression discontinuity 1 Introduction McCrary (2008) introduced the idea of manipulation testing in the context of regression discontinuity (RD) designs. Regression Discontinuity: Model De nitions T is a treatment indicator: T = 1 if treated, T = 0 if untreated. Publisher: University of Bern, Department of Economics, Bern. Now consider a regression discontinuity (RD) design where the running variable is the test score z i, the treatment is diploma receipt as indicated by d i, and the assignment rule is given by equation 2. What video, book or resource do you suggest that can fairly simply explain how to set up the code in Stata for regression discontinuity and explain the interpretation of results? It is one of the most credible quasi-experimental research designs for identiﬁcation, estimation, and inference of treatment eﬀects (local to the cutoﬀ). For example, test scores of students can take any value between 0-100 . 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. Cattaneo, Keele, Titiunik and Vazquez-Bare (2020): Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs. 2. Among other things, it established the RDD estimates local average treatment effects around the cutoff point, where treatment and comparison units are most similar. 18:10. From: International Encyclopedia of Education (Third Edition), 2010. The units to the left and right of the cutoff look more and more similar as they near the cutoff. Regression Discontinuity: Model De nitions T is a treatment indicator: T = 1 if treated, T = 0 if untreated. 2004. When you use software (like R, Stata, SPSS, etc.) Best answer. We consider a regression discontinuity (RD) design where the treatment is received if a score is above a cutoff, but the cutoff may vary for each unit in the sample instead of being equal for all units. Interested in causal effect of a binary (Yes/No) treatment variable \ (D\) on outcome \ (Y\) E.g. Missed opportunity. uence results In some cases, however, it may matter Gelman and Inbems (2014) subtle paper: \Why High-order 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, and we recommend researchers do Regression discontinuity analysis is a statistical tool that allows researchers to examine the effectiveness of the treatment in such studies. The cut-off is based on a continuous rather than categorical variable. The project focuses on 'regression discontinuity' (RD) models, which are very widely used in practice. Displays log odds of regressors. This multi-cutoff regression discontinuity design is very common in empirical work, and researchers often normalize the score variable and use the zero cutoff on the normalized score for all observations to estimate a pooled RD treatment effect. Regression discontinuity designs are of increasing interest in epidemiology as a method of analyzing natural experiments, evaluating interventions, and supporting causal inference in the absence of randomized trials. the regression-discontinuity analysis may be regarded as a substitute" (Thistlethwaite and Campbell 1960, p. 310). Regression discontinuity is used to estimate the effect of a program when the following conditions are met: There is a discrete cut-off (e.g. To be considered as good as a local experiment for the units close enough to the cutoff point, one must use a very narrow bandwidth and drop the assignment variable (or a function of it) from . 2. IES (2008) defines regression discontinuity designs as "designs in which participants are assigned to the intervention and the control conditions based on a cut-off score on a pre-intervention measure that typically assesses need or merit. Regression Discontinuity (RD) designs were ﬁrst introduce d by Thistlethwaite and Campbell (1960) as a way of estimating treatment effects in a non-experimental setting where treatment is determined by whether an observed "assignment" variable (also referred to in the l iterature as the "forcing" variable or the "run- Regression discontinuity (RD) designs for evaluating causal eﬀects of interventions, where as-signment is determined at least partly by the value of an observed covariate lying on either side of a threshold, were introduced by Thistlewaite and Campbell (1960). In order to interpret the results of an RD design, one must know the nature of the assignment variable, who received the program and the nature of the outcome measure. tensions and results, covering a nonseparable RD model with unobserved unit-speciﬁc heterogeneity (Lee 2008), kink RD designs (Card et al. Figure 12. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, In both cases, we illustrate that exploiting the regression discontinuity design reveals negative e ects of the marketing campaigns that would not have been uncovered using other approaches. We illustrate our methodological results with three empirical examples based on vote shares, population, and test scores. HE regression discontinuity (RD) design is widely used in economics, political science, and many other social, behavioral, biomedical, and statistical sciences. There has been a growing use of regression discontinuity design (RDD), introduced by Thistlewaite and Campbell (1960), in evaluating the impacts of development programs. PDF McCrary, J. Regression discontinuity (RD) analysis is a rigorous nonexperimental1approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cut-point. We provide a review of the main assumptions and key challenges faced when adopting an RDD. The Regression-Discontinuity design is seen as a useful method for determining whether a program or treatment is effective. 1 like 0 dislike. This research grant focused on the development of new methods for the statistical analysis of micro-level data employing the 'regression discontinuity' (RD) design, a research strategy widely used in practice across many disciplines in the social, behavioral, biomedical, and statistical sciences. Part 4 considers how to interpret and generalize results from analyses. (2008): "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, 142(2 . IES (2008) defines regression discontinuity designs as "designs in which participants are assigned to the intervention and the control conditions based on a cut-off score on a pre-intervention measure that typically assesses need or merit. of regression discontinuity settings in which complete manipulation is a potential threat to validity include Hahn, Todd and van der Klaauw (1999) and Jacob and Lefgren (2004), for example. Basically, RD is a regression that involves dummy variable and interaction term. Lee, D. S., and T. Lemieux 2014, Chapter 14: Regression discontinuity designs in social sciences[C], in H. Best,C. RD designs are also easy to present, interpret, and falsify, which are features that have contributed to their . The Regression Discontinuity (RD) design is widely used in Economics and other social, behavioral, biomedical, and statistical sciences. Here's how I like to play with discontinuities: Discontinuities can be estimated using hinge functions. In statistics, regression analysis is a technique that can be used to analyze the relationship between predictor variables and a response variable. The course focuses on methodology and empirical practice, and will not discuss much of the statistical and econometric theory underlying the results. A regression discontinuity design returns a plausible estimate of the causal effect of a treatment under certain conditions. r regression-discontinuity. . Journal of Politics 78(4): 1229-1248. Consider a setting where each unit in a random sample from a large population is assigned to one of two groups depending on whether one of their observed covariates exceeds a known cutoﬀ. with score equal to x, Xi = x !treated. I am trying to interpret a fuzzy Regression Discontinuity Design. For example, test scores of students can take any value between 0-100 . score, poverty index, class size) that divides the sample into treatment and control groups. academic performance in higher grades. The point of this post is (a) to talk about how to do a better analysis using the general perspective of observational studies, and also (b) to free people from thinking that the simplistic regression discontinuity (in which only x is adjusted for, and in which there's no concern about the fitted functional form of the regression) is the . for which the regression-discontinuity analysis may be regarded as a substi-tute" (Thistlethwaite & Campbell, 1960, p. 310). to perform a regression analysis, you will receive a regression table as output that summarize the results of the regression.. Arguably the most important numbers in the output of the regression . A regression of wages on a diploma indicator consistently estimates the diploma wage premium Δ. This paper shows that it is possible to identify the RD model treatment effect without a discontinuity. • 3) is rarely used. SW Ch 8 4/54/ Nonlinear Regression - General Ideas If a relation between Y and X is nonlinear: The effect on Y of a change in X depends on the value of X - that is, the marginal effect of X is not constant A linear regression is mis-specified: the functional form In this Element and its accompanying Element, Matias D. 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 . Regression Discontinuity Design 1 Motivating example • Many districts have summer school to help kids improve outcomes between grades - Enrichment, or - Assist those lagging • Research question: does summer school improve outcomes:se•Vblaira - x=1 is summer school after grade g - y = test score in grade g+1 2 • Equation of interest Share. Given that the design meets all assumptions and conditions outlined . Regression Discontinuity Design - an overview . Regression Discontinuity Design (RDD) is a quasi-experimental evaluation option that measures the impact of an intervention, or treatment, by applying a treatment assignment mechanism based on a continuous eligibility index which is a variable with a continuous distribution. The learning objectives of this module are: regression discontinuity design generated by school-entry cutoffs and school records from one district in Florida, we find positive spillover effects from an older to a younger child in less affluent families and negative spillover effects from a younger to an older child in more affluent families. Regression discontinuity is used to estimate the effect of a program when the following conditions are met: There is a discrete cut-off (e.g. 10 Regression Discontinuity Designs. Q. academic performance in higher grades. Sharp Regression Discontinuity Design Treatment e•ect • A special situation occurs at the cuto• X = x, the only point at which we may "almost" observe both curves • Two groups of units:. Regression discontinuity analysis is used for studies in which participants are assigned to treatment conditions based on a known assignment rule rather than randomly being assigned to conditions. The results for this model are shown in Figure 12. Main results. We assume (1) only the treatment causes discontinuity or jump in the dependent Regression Discontinuity Design. regression discontinuity analysis can identify an average effect of treatment for a population and how . 5 Propositions 2 and 3 of Lee (2007) establish that, under mild regularity conditions, identiﬁcation of We analyzed HIV cohort data from ART programs in six Southern African countries using regression discontinuity to estimate the effect of national Treat . Cite. I've modified the results of my numbers, but I would like to understand the meaning of the output of an RDestimate object. new www.sciencedirect.com. Regression Discontinuity (RD) models identify local treatment effects by as-sociating a discrete change in the mean outcome with a corresponding discrete change in the probability of treatment at a known threshold of a running vari-able. This kind of statistical model is used to estimate the effect of a treatment on an outcome when unobserved factors may threaten the validity of the statistical analysis. 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. • Participants in program evaluation studies commonly receive new program services or experience policy changes because they reside in a particular city, The RD design is implemented by running the . score, poverty index, class size) that divides the sample into treatment and control groups. Best answer. Regression Discontinuity 1. Take the simple case of a linear relationship that jumps discontinuously at some point. Regression Discontinuity: Situation. The model for a straight line is (or ought to be :) familiar (leaving out error): y = β 0 + β x x. We can see that in this model the treatment effect estimate is now 9.89 (SE = .95). The interpretation of RD designs as local experiments developed by Lee (2008) has been very inﬂuential, both conceptually and practically. Don't have random assignment. Y is an outcome, e.g. Wolf eds, The sage handbook of regression analysis and causal inference. example: Jacob and Lefgren (2004), T indicates going to summer school. In this lecture we will consider both "sharp" and "fuzzy" RDDs. In particular, the validity of the design hinges on the assumption that the only thing that changes discontinuously as a function of the running variable is whether an observational unit is treated. Part 5 regression discontinuity considers some important frontiers for future regression discontinuity research. Regression discontinuity is used to solve this problem and assess whether or not we observe a treatment effect. Regression discontinuity design - Wikipedia great en.wikipedia.org. A brief description of the course, along with references to further readings, is given below. Called "incumbency advantage" in poli sci. Since this design exploits these discontinuouschanges in a treatment assignment variable (also known as a forcing or running variable), we call it a regression discontinuity design. Interpretation of Results.. 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