Research on Matching Methods for Causal Inference in Experimental and Observational Studies

The estimation of causal effects is a central goal of social science research. In this project, we develop matching methods that can help empirical researchers conduct reliable and efficient causal inference in both experimental and observational studies.

First, we clarify the misunderstandings commonly held by applied researchers about matching and propensity score methods. We introduce a general framework where matching methods can be considered as a preprocessing procedure that improves the robustness of parametric regression models. In this view, matching methods should not be thought as an alternative to regression techniques. We also address the other methodlogical issues such as balance checking and standard error calculation. An easy-to-use package is developed in the free software language R that implements various matching methods.

Second, we show that matching methods can be useful for experimetal studies. In particular, matched-pair designs can recoup the loss of efficiency that is common in cluster randomized experiments. The matched-pair cluster randomized design provides a robust and efficient method for estimating treatment effects in the presence of interference among units. We apply our proposed methodology to the randomized evaluation of the Mexican universal health insurance program. This program evaluation is one of the largest such health policy experiments in the history.

Third, we consider the relationships between matching methods and linear fixed effects estimators. Our analysis shows that fixed effects estimators, which are the primary workforce of applied social scientists for panel data and other analyses, can be shown to be equivalent to particular matching estimators. This analysis in turn provides us insights about the limitations of fixed effects estimators. Finally, we address these limitations by developing matching methods for the time-series cross-sectional data.

Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. (2007). ``Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.'' Political Analysis, Vol. 15, No.3 (Summer), pp. 199-236.

Imai, Kosuke, Gary King, and Elizabeth A. Stuart. (2008). ``Misunderstandings among Experimentalists and Observationalists about Causal Inference.'' Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 171, No. 2 (April), pp. 481-502.

Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth Stuart. (2011). ``MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.'' Journal of Statistical Software, Vol. 42 (Special Volume on Political Methodology), No. 8 (June), pp. 1-28.

Imai, Kosuke. (2008). ``Variance Identification and Efficiency Analysis in Randomized Experiments under the Matched-Pair Design.'' Statistics in Medicine, Vol. 27, No. 24 (October), pp. 4857-4873.

Imai, Kosuke, Gary King, and Clayton Nall. (2009). ``The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation.'' (with discussions and rejoinder) Statistical Science, Vol. 24, No. 1 (February), pp. 29-53.

Imai, Kosuke, Gary King, and Clayton Nall. (2009). ``Rejoinder: Matched Pairs and the Future of Cluster-Randomized Experiments.'' Statistical Science, Vol. 24, No. 1 (February), pp. 65-72.

King, Gary, Emmanuela Gakidou, Kosuke Imai, Jason Lakin, Ryan T. Moore, Clayton Nall, Nirmala Ravishankar, Manett Vargas, Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila, and Héctor Hernández Llamas. (2009). ``Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme," (with a comment) The Lancet, Vol. 373, No. 9673 (April), pp. 1447-1454.

Imai, Kosuke, and Zhichao Jiang. (2018). ``A Sensitivity Analysis for Missing Outcomes under the Matched-Pairs Design.'' Statistics in Medicine, Vol. 37, No. 20 (September), pp. 2907-2922.

Tarr, Alexander and Kosuke Imai. ``Estimating Average Treatment Effects with Support Vector Machines.'' Statistics in Medicine, Forthcoming.

Imai, Kosuke, and In Song Kim. (2019). ``When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?'' American Journal of Political Science, Vol. 63, No. 2 (April), pp. 467-490.

Imai, Kosuke and In Song Kim. (2021). ``On the Use of Two-way Fixed Effects Regression Models for Causal Inference with Panel Data.'' Political Analysis, Vol. 29, No. 3 (July), pp. 405-415.

Imai, Kosuke, In Song Kim, and Erik Wang. (2023). ``Matching Methods for Causal Inference with Time-Series Cross-Sectional Data.'' American Journal of Political Science, Vol. 67, No. 3 (July), pp. 587-605.

Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth Stuart. ``MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.'' available through The Comprehensive R Archive Network. 2005-2009.

Kim, In Song and Kosuke Imai. ``wfe: Weighted Linear Fixed Effects Estimators for Causal Infernece.'' available through The Comprehensive R Archive Network. 2011.

National Science Foundation, (2006-2009). ``Collaborative Research: Generalized Propensity Score Methods,'' (Methodology, Measurement and Statistics Program; SES-0550873).