insongkim/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.

Getting started

Package details

Maintainer
LicenseGPL(>= 3)
Version1.0.1
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("insongkim/PanelMatch")
insongkim/PanelMatch documentation built on Aug. 20, 2020, 4:22 a.m.