Cited by 349Advanced Causal Inference TechniquesSpatial and Panel Data AnalysisQualitative Comparative Analysis Research

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

Imai, Kosuke, Kim, In Song, Wang, Erik H.·2023·American Journal of Political Science

DOI 10.1111/ajps.12685

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Abstract

Matching 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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Authors: Imai, K., Kim, I. S., & Wang, E. H.
Affiliations: Harvard University (US)
Massachusetts Institute of Technology (US)
Australian National University (AU)
Corresponding author: Imai, Kosuke
ORCID: Imai, Kosuke
Kim, In Song
Venue: American Journal of Political Science
Publisher: Wiley
Published: 2021-12-11
Volume: 67
Issue: 3
Pages: 587-605
ISSN: 0092-5853 (ISSN-L)
1540-5907
DOI: 10.1111/ajps.12685
OpenAlex: W4200386818
Cited by: 349
References: 44
Citations by year: 2026: 63 · 2025: 79 · 2024: 80 · 2023: 68 · 2022: 32 · 2021: 13
FWCI: 35.67
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: American Journal of Political Science (publishedVersion)
Funders: Agence Nationale de la Recherche (ANR-17-EURE-0010)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Social Sciences › Sociology and Political Science › Qualitative Comparative Analysis Research
Keywords: matching methods, causal inference, time-series cross-sectional data, treatment effect estimation, covariate balance, difference-in-differences, average treatment effect, model dependence, treatment history

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

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