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) <> 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.


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1.0.0 by In Song Kim, 4 months ago

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Authors: In Song Kim [aut, cre] , Adam Rauh [aut] , Erik Wang [aut] , Kosuke Imai [aut]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports Rcpp, data.table, ggplot2, CBPS, stats, graphics, grDevices, MASS, Matrix, methods

Suggests knitr, rmarkdown, testthat

Linking to RcppArmadillo, Rcpp, RcppEigen

System requirements: C++11

See at CRAN