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journal article Dec 11, 2021

Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data

Kosuke Imai ORCID In Song Kim Erik H. Wang
American Journal of Political Science Vol. 67 No. 3 pp. 587-605 · JSTOR
View at Publisher Save 10.1111/ajps.12685
Abstract
AbstractMatching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics. Although they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analysing time‐series cross‐sectional data. We fill this methodological gap. In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the prespecified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values. Assessing the quality of matches is done by examining covariate balance. Finally, we estimate both short‐term and long‐term average treatment effects using the difference‐in‐differences estimator, accounting for a time trend. We illustrate the proposed methodology through simulation and empirical studies. An open‐source software package is available for implementing the proposed methods.
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References
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Metrics
217
Citations
46
References
Details
Published
Dec 11, 2021
Vol/Issue
67(3)
Pages
587-605
License
View
Authors
K
Kosuke Imai

Harvard University

I
In Song Kim

Massachusetts Institute of Technology

E
Erik H. Wang

Australian National University

Cite This Article
Kosuke Imai, In Song Kim, Erik H. Wang (2021). Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data. American Journal of Political Science, 67(3), 587-605. https://doi.org/10.1111/ajps.12685
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