Matching Methods for Causal Inference with Time-Series Cross-Sectional Data
Implements a set of methodological tools that enable researchers to apply matching methods to time-series cross-sectional data. Imai, Kim, and Wang (2023) proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching is done, both short-term and long-term average treatment effects for the treated observations can be estimated with standard errors. The package also offers a variety of diagnostic and visualization functions to assess the credibility of results. package
Imai, Kosuke, In Song Kim and Erik Wang. (2023)
Useful links:
In Song Kim insong@mit.edu, Erik Wang haixiao@Princeton.edu, Adam Rauh amrauh@umich.edu, and Kosuke Imai imai@harvard.edu
Maintainer: In Song Kim insong@mit.edu
Useful links