Modified Causal Forest
Type
working paper
Date Issued
2022-08
Author(s)
Abstract
Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops estimation and inference procedures for multiple treatment models in a selection-on-observed-variables framework by modifying the Causal Forest approach (Wager and Athey, 2018) in several dimensions. The new estimators have desirable theoretical, computational, and practical properties for various aggregation levels of the causal effects. While an Empirical Monte Carlo study suggests that they outperform previously suggested estimators, an application to the evaluation of an active labour market programme shows their value for applied research.
Keywords
Causal machine learning
statistical learning
conditional average treatment effects
individualized treatment effects
multiple treatments
selection-on-observed-variables
Official URL
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Name
mcf-arxiv.pdf
Size
1.59 MB
Format
Adobe PDF
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