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Replication data for: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication data for: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
 
Identifier https://doi.org/10.7910/DVN/RWUY8G
 
Creator Ho, Daniel E.
Imai, Kosuke
King, Gary
Stuart, Elizabeth A.
 
Publisher Harvard Dataverse
 
Description Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that
the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author’s favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological literature are often grossly misinterpreted. We explain how to avoid these misinterpretations and propose a unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply the best parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.


See also: Causal Inference
 
Subject Social Sciences
 
Date 2006