Working Paper: NBER ID: w13039
Authors: Guido Imbens; Thomas Lemieux
Abstract: 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. These designs were first introduced in the evaluation literature by Thistlewaite and Campbell (1960). With the exception of a few unpublished theoretical papers, these methods did not attract much attention in the economics literature until recently. Starting in the late 1990s, there has been a large number of studies in economics applying and extending RD methods. In this paper we review some of the practical and theoretical issues involved in the implementation of RD methods.
Keywords: No keywords provided
JEL Codes: C14; C21
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| sharp regression discontinuity design (SRD) (C24) | discontinuity in outcomes (D52) |
| discontinuity in conditional expectation of the outcome at cutoff (C24) | average causal effect of the treatment (C22) |
| smooth relationship between covariate and potential outcomes around cutoff (C24) | validity of causal inference in RD designs (C90) |
| fuzzy regression discontinuity design (FRD) (C24) | average causal effect can be estimated (C13) |
| testing for continuity in covariates (C29) | validation of RD design (C90) |
| continuity in covariates (C32) | ensuring observed discontinuities are causal (C22) |