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Cited by 4,205

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

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

Open access
Ho, Daniel E., Imai, Kosuke, King, Gary, Stuart, Elizabeth A.·2007·Political Analysis
DOI 10.1093/pan/mpl013
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Abstract

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.

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Cited by

Metadata

Authors
Ho, D. E., Imai, K., King, G., & Stuart, E. A.
Venue
Political Analysis
Publisher
Cambridge University Press (CUP)
Published
2007
Volume
15
Issue
3
Pages
199-236
ISSN
  • 1047-1987 (ISSN-L)
  • 1476-4989
DOI
10.1093/pan/mpl013
OpenAlex
W2139577967
Cited by
4,205
References
15
FWCI
78.08
Type
journal article
Language
en
Open access
bronze
Licence
www.cambridge.org/core/terms (unspecified, 3656 d)

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