Implements a set of methodological tools that enable researchers to apply matching methods to timeseries crosssectional data. Imai, Kim, and Wang (2018) <http://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a nonparametric generalization of the differenceindifferences 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 shortterm and longterm 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 


Maintainer  
License  GPL(>= 3) 
Version  1.0.1 
Package repository  View on GitHub 
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