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

Item Type Forthcoming
Abstract 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.
Authors Lechner, Michael; Bodory, Hugo & Busshoff, Hannah
Journal or Publication Title Entropy
Language English
Keywords econometrics software; causal machine learning; statistical learning; conditional average treatment effects; individualized treatment effects; multiple treatments; selection-on-observables
Subjects information management
econometrics
statistics
HSG Classification contribution to scientific community
HSG Profile Area SEPS - Economic Policy
Refereed No
Date 28 July 2022
Publisher MDPI
Volume 24
Number 1039
ISSN 1099-4300
Publisher DOI https://doi.org/10.3390/e24081039
Official URL https://www.mdpi.com/1099-4300/24/8/1039
Depositing User Stefanie Kohnle
Date Deposited 17 Oct 2022 09:17
Last Modified 28 Feb 2023 12:40
URI: https://www.alexandria.unisg.ch/publications/267594

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Citation

Lechner, Michael; Bodory, Hugo & Busshoff, Hannah (2022) High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal Forest. Entropy, 24 (1039). ISSN 1099-4300

Statistics

https://www.alexandria.unisg.ch/id/eprint/267594
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