PanelMatch: 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 (2018) <http://web.mit.edu/insong/www/pdf/tscs.pdf> 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 can be estimated with standard errors. The package also offers a visualization technique that allows researchers to assess the quality of matches by examining the resulting covariate balance.

Package details

AuthorIn Song Kim [aut, cre], Adam Rauh [aut], Erik Wang [aut], Kosuke Imai [aut]
MaintainerIn Song Kim <insong@mit.edu>
LicenseGPL (>= 3)
Version1.0.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("PanelMatch")

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PanelMatch documentation built on Feb. 28, 2020, 5:07 p.m.