🤖 AI Summary
Standard Causal Forests applied to panel data often yield spurious heterogeneity in treatment effects due to their neglect of unit and time fixed effects. This work proposes a node-level residualization strategy that locally removes fixed effects within each candidate split during tree construction, thereby avoiding the bias introduced by global demeaning. Building upon the Causal Forests framework, the method integrates fixed effects modeling with random forests to efficiently estimate heterogeneous treatment effects. The authors implement and publicly release the approach as the Python package `causalfe`. Simulation studies demonstrate that the proposed method accurately recovers true treatment effect heterogeneity across a range of data-generating processes and substantially outperforms conventional Causal Forests.
📝 Abstract
The causalfe package provides a Python implementation of Causal Forests with Fixed Effects (CFFE) for estimating heterogeneous treatment effects in panel data settings. Standard causal forest methods struggle with panel data because unit and time fixed effects induce spurious heterogeneity in treatment effect estimates. The CFFE approach addresses this by performing node-level residualization during tree construction, removing fixed effects within each candidate split rather than globally. This paper describes the methodology, documents the software interface, and demonstrates the package through simulation studies that validate the estimator's performance under various data generating processes.