causalfe: Causal Forests with Fixed Effects in Python

TL;DR

Causalfe introduces a Python package implementing Causal Forests with Fixed Effects, enabling accurate estimation of heterogeneous treatment effects in panel data by addressing fixed effects through node-level residualization.

Contribution

It presents a novel method for fixed effects in causal forests using node-level residualization, along with a software implementation and validation through simulations.

Findings

Effective removal of fixed effects in treatment effect estimation

Validated performance across multiple simulated data scenarios

Provides a practical tool for panel data causal inference

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.