Modified Causal Forests for Estimating Heterogeneous Causal Effects

TL;DR

This paper introduces modified Causal Forest methods for more accurate estimation of heterogeneous causal effects across different levels of granularity, enhancing decision-making in policy and business contexts.

Contribution

It develops new estimation and inference procedures for multiple treatment models by modifying the Causal Forest approach, improving theoretical, computational, and practical properties.

Findings

Outperforms previous estimators in Monte Carlo simulations

Demonstrates practical utility in evaluating a labour market program

Provides more reliable estimates of causal heterogeneity

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.