The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data
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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Cites
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Biases in dynamic models with fixed effects
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Marginal Structural Models and Causal Inference in Epidemiology
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Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
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Self-citationCausal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
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Metadata
- 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
- 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
- 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
- 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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