Opiates for the Matches: Matching Methods for Causal Inference

被引:172
作者
Sekhon, Jasjeet S. [1 ]
机构
[1] Univ Calif Berkeley, Survey Res Ctr, Travers Dept Polit Sci, Berkeley, CA 94720 USA
关键词
causal inference; matching; Neyman-Rubin model; PROPENSITY-SCORE; REGRESSION ADJUSTMENTS; RANDOMIZED EXPERIMENTS; NATURAL EXPERIMENTS; STATISTICAL-MODELS; BIAS REDUCTION; DESIGN; IDENTIFICATION; DISTRIBUTIONS; ESTIMATORS;
D O I
10.1146/annurev.polisci.11.060606.135444
中图分类号
D0 [政治学、政治理论];
学科分类号
0302 ; 030201 ;
摘要
In recent years, there has been a burst of innovative work. on methods for estimating causal effects using observational data. Much of this work has extended and brought a renewed focus on old approaches Such as matching, which is the focus of this review. The new developments highlight an old tension in the social sciences: a focus on research design versus a focus on quantitative models. This realization, along with the renewed interest in field experiments, has marked the return of foundational questions as opposed to a fascination with the latest estimator. I use Studies Of get-out-the-vote interventions to exemplify this development. without an experiment, natural experiment, a discontinuity, or some other strong design, no amount of econometric or statistical modeling can make the move from correlation to causation persuasive.
引用
收藏
页码:487 / 508
页数:22
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