PanelMatch: Matching Methods for Causal Inference with Time-Series Cross-Section Data
Abstract
Analyzing time-series cross-sectional (also known as longitudinal or panel) data is an important process across a number of fields, including the social sciences, economics, finance, and medicine. PanelMatch is an R package that implements a set of tools enabling researchers to apply matching methods for causal inference with time-series cross-sectional data. Relative to other commonly used methods for longitudinal analyses, like regression with fixed effects, the matching-based approach implemented in PanelMatch makes fewer parametric assumptions and offers more diagnostics. In this paper, we discuss the PanelMatch package, showing users a recommended pipeline for doing causal inference analysis with it and highlighting useful diagnostic and visualization tools.
Cite
@article{arxiv.2503.02073,
title = {PanelMatch: Matching Methods for Causal Inference with Time-Series Cross-Section Data},
author = {Adam Rauh and In Song Kim and Kosuke Imai},
journal= {arXiv preprint arXiv:2503.02073},
year = {2025}
}