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 (2023) 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 and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results.
- Version3.0.0
- R versionR (≥ 2.14.0)
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release03/03/2025
Documentation
Team
In Song Kim
MaintainerShow author detailsAdam Rauh
Show author detailsRolesAuthorErik Wang
Show author detailsRolesAuthorKosuke Imai
Insights
Last 30 days
This package has been downloaded 730 times in the last 30 days. Not bad! The download count is somewhere between 'small-town buzz' and 'moderate academic conference'. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 26 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 6,773 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Jul 21, 2024 with 146 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
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Dependencies
- Imports8 packages
- Suggests3 packages
- Linking To3 packages