High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal Forest
Journal
Entropy
ISSN
1099-4300
Type
forthcoming
Date Issued
2022-07-28
Author(s)
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.
Language
English
Keywords
econometrics software
causal machine learning
statistical learning
conditional average treatment effects
individualized treatment effects
multiple treatments
selection-on-observables
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Economic Policy
Refereed
No
Publisher
MDPI
Volume
24
Number
1039
Official URL
Eprints ID
267594
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open.access
Name
entropy-24-01039-v3.pdf
Size
1.86 MB
Format
Adobe PDF
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