Modified Causal Forests for Estimating Heterogeneous Causal Effects

arXiv 22 Dec 2018 · Econometrics · 18 citations (OpenAlex)

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 new estimation and inference procedures for multiple treatment models in a selection-on-observables framework by modifying the Causal Forest approach suggested by 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 the value of the new methods for applied research.

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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

	Citing paper	Intensity	Mentions	Sections
1	Random Forest Estimation of the Ordered Choice Model	1.000	11	4
2	Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance	1.000	11	5
3	How do applied researchers use the Causal Forest? A methodological review	0.843	3	3
4	Aggregation Trees	0.737	3	2
5	1908.08779	0.585	3	1
6	Transparency challenges in policy evaluation with causal machine learning –- improving usability and accountability	0.511	2	1
7	Estimation of Conditional Average Treatment Effects with High-Dimensional Data	0.405	1	1
8	How have German University Tuition Fees Affected Enrollment Rates: Robust Model Selection and Design-based Inference in High-Dimensions	0.405	1	1
9	Sample Fit Reliability	0.405	1	1
10	2606.24785	0.405	1	1