High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal Forest

Entropy (Basel). 2022 Jul 28;24(8):1039. doi: 10.3390/e24081039.


There is great demand for inferring causal effect heterogeneity and for open-source statistical software, which is readily available for practitioners. The mcf package is an open-source Python package that implements Modified Causal Forest (mcf), a causal machine learner. We replicate three well-known studies in the fields of epidemiology, medicine, and labor economics to demonstrate that our mcf package produces aggregate treatment effects, which align with previous results, and in addition, provides novel insights on causal effect heterogeneity. For all resolutions of treatment effects estimation, which can be identified, the mcf package provides inference. We conclude that the mcf constitutes a practical and extensive tool for a modern causal heterogeneous effects analysis.

Keywords: causal machine learning; conditional average treatment effects; econometrics software; individualized treatment effects; multiple treatments; selection-on-observables; statistical learning.

Grants and funding

Hannah Busshoff and Michael Lechner gratefully acknowledge financial support from the Swiss National Science Foundation (SNSF) (grant number SNSF 407740_187301).