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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Likewise, investors from the Gulf region increasingly invest in nearby countries to foster 'autocratic stability in the neighbourhood.' 53”
From Authoritarian collaboration and repression in the digital age: balancing foreign direct investment and control in internet infrastructure (2026) · cites this paper as [53]
Citations
Cites
Arellano, M., & Bond, S. (1991) · The Review of Economic Studies · doi:10.2307/2297968 · Cited by 33,272
How Much Should We Trust Differences-in-Differences Estimates?
Bertrand, M., Duflo, E., & Mullainathan, S. (2004) · The Quarterly Journal of Economics · doi:10.1162/003355304772839588 · Cited by 10,641
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2009) · doi:10.1515/9781400829828 · Cited by 9,467
Biases in dynamic models with fixed effects
Nickell, S. (1981) · Econometrica · doi:10.2307/1911408 · Cited by 8,735
What To Do (and Not to Do) with Time-Series Cross-Section Data
Beck, N., & Katz, J. N. (1995) · American Political Science Review · doi:10.2307/2082979 · Cited by 6,840
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581
Abadie, A., Diamond, A., & Hainmueller, J. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.ap08746 · Cited by 5,513
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
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,285
Self-citationCausal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
Hernán, M. A., & Robins, J. M. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv254 · Cited by 3,115
Semiparametric Difference-in-Differences Estimators
Abadie, A. (2005) · The Review of Economic Studies · doi:10.1111/0034-6527.00321 · Cited by 2,286
Acemoğlu, D., Naidu, S., Restrepo, P., & Robinson, J. A. (2018) · Journal of Political Economy · doi:10.1086/700936 · Cited by 1,652
Bias-Corrected Matching Estimators for Average Treatment Effects
Abadie, A., & Imbens, G. W. (2011) · Journal of Business & Economic Statistics · doi:10.1198/jbes.2009.07333 · Cited by 1,416
Covariate Balancing Propensity Score
Imai, K., & Ratkovic, M. (2013) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12027 · Cited by 1,379
Self-citationRosenbaum, P., & Rubin, D. B. (1983) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1983.tb01242.x · Cited by 1,128
Diamond, A., & Sekhon, J. S. (2012) · The Review of Economics and Statistics · doi:10.1162/rest_a_00318 · Cited by 1,100
Multivariate Matching Methods That Are Monotonic Imbalance Bounding
Iacus, S., King, G., & Porro, G. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.tm09599 · Cited by 998
Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models
Xu, Y. (2017) · Political Analysis · doi:10.1017/pan.2016.2 · Cited by 938
Design-based analysis in Difference-In-Differences settings with staggered adoption
Athey, S., & Imbens, G. W. (2022) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.10.012 · Cited by 917
On the Failure of the Bootstrap for Matching Estimators
Abadie, A., & Imbens, G. W. (2008) · Econometrica · doi:10.3982/ecta6474 · Cited by 814
Toward Causal Inference With Interference
Hudgens, M. G., & Halloran, M. E. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000292 · Cited by 768
Difference-in-Differences with Variation in Treatment Timing
Goodman-Bacon, A. (2018) · doi:10.3386/w25018 · Cited by 757
Full Matching in an Observational Study of Coaching for the SAT
Hansen, B. B. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000000647 · Cited by 648
On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data
Imai, K., & Kim, I. S. (2021) · Political Analysis · doi:10.1017/pan.2020.33 · Cited by 623
Self-citationCorrecting for non-compliance in randomized trials using structural nested mean models
Robins, J. M. (1994) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929408831393 · Cited by 534
The Augmented Synthetic Control Method
Ben-Michael, E., Feller, A., & Rothstein, J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1929245 · Cited by 462
On causal inference in the presence of interference
Tchetgen, E. J. T., & VanderWeele, T. J. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280210386779 · Cited by 423
Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis
Doudchenko, N., & Imbens, G. (2016) · doi:10.3386/w22791 · Cited by 410
When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?
Imai, K., & Kim, I. (2019) · American Journal of Political Science · doi:10.1111/ajps.12417 · Cited by 407
Self-citationTwo-way Fixed Effects Estimators with Heterogeneous Treatment Effects
de Chaisemartin, C., & D'Haultfoeuille, X. (2019) · doi:10.3386/w25904 · Cited by 350
Aronow, P. M., & Samii, C. (2017) · The Annals of Applied Statistics · doi:10.1214/16-aoas1005 · Cited by 347
Matched Sampling for Causal Effects: Contributor Acknowledgments
Rubin, D. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810725 · Cited by 287
Democracy, War, and Wealth: Lessons from Two Centuries of Inheritance Taxation
Scheve, K. F., & Stasavage, D. (2012) · American Political Science Review · doi:10.1017/s0003055411000517 · Cited by 268
Zubizarreta, J. R. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.703874 · Cited by 213
Li, Y.-F., Propert, K., & Rosenbaum, P. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753208573 · Cited by 197
Rosenbaum, P. R., Ross, R. N., & Silber, J. H. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001059 · Cited by 163
How to Make Causal Inferences with Time-Series Cross-Sectional Data Under Selection on Observables
Blackwell, M., & Glynn, A. (2018) · American Political Science Review · doi:10.1017/s0003055418000357 · Cited by 131
Estimating Dynamic Treatment Effects in Event Studies With Heterogeneous Treatment Effects
Sun, L., & Abraham, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3158747 · Cited by 118
Asymptotic Efficiency in Parametric Structural Models with Parameter-Dependent Support
Hirano, K., & Porter, J. R. (2003) · Econometrica · doi:10.1111/1468-0262.00451 · Cited by 111
Robust Estimation of Inverse Probability Weights for Marginal Structural Models
Imai, K., & Ratkovic, M. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.956872 · Cited by 94
Self-citationBootstrap Inference of Matching Estimators for Average Treatment Effects
Otsu, T., & Rai, Y. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1231613 · Cited by 64
Causal Inference With Interference and Noncompliance in Two-Stage Randomized Experiments
Imai, K., Jiang, Z., & Malani, A. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1775612 · Cited by 55
Self-citationSynthetic Controls and Weighted Event Studies with Staggered AdoptionPreprint
Ben‐Michael, E., Feller, A., & Rothstein, J. (2019) · arXiv (Cornell University) · Cited by 12
Flagged first, then by citations
Cited by
Sorted by citations
Action needed to make carbon offsets from forest conservation work for climate change mitigation
West, T. A. P., Wunder, S., Sills, E. O., Börner, J., Rifai, S. W., Neidermeier, A. N., Frey, G., & Kontoleon, A. V. (2023) · Science · doi:10.1126/science.ade3535 · Cited by 466
Liu, L., Wang, Y., & Xu, Y. (2024) · American Journal of Political Science · doi:10.1111/ajps.12723 · Cited by 390
Systematic Evaluation of State Policy Interventions Targeting the US Opioid Epidemic, 2007-2018
Lee, B., Zhao, W., Yang, K.-C., Ahn, Y., & Perry, B. L. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.36687 · Cited by 186
Liu, L., Wang, Y., & Xu, Y. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3555463 · Cited by 142
The economic consequences of major tax cuts for the rich
Hope, D., & Limberg, J. (2021) · Socio-Economic Review · doi:10.1093/ser/mwab061 · Cited by 121
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
