PanelMatch-package: Matching Methods for Causal Inference with Time-Series...

In PanelMatch: Matching Methods for Causal Inference with Time-Series Cross-Sectional Data

Matching Methods for Causal Inference with Time-Series Cross-Sectional Data

Description

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 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.

Author(s)

In Song Kim <[e-mail]>, Erik Wang <[e-mail]>, Adam Rauh <[e-mail]>, and Kosuke Imai <[e-mail]>

Maintainer: In Song Kim [e-mail]

References

Imai, Kosuke, In Song Kim and Erik Wang. (2023)

See Also

Useful links:

• Report bugs at https://github.com/insongkim/PanelMatch/issues

PanelMatch documentation built on June 22, 2024, 10:32 a.m.