Modified Causal Forests for Estimating Heterogeneous Causal Effects
This work provides improved tools for decision-makers in policy and business to assess causal effects at granular levels, though it is incremental as it builds on existing Causal Forest methods.
The paper tackled the problem of estimating heterogeneous causal effects for multiple treatments by modifying the Causal Forest approach, resulting in new estimators that outperform previous methods in simulations and demonstrate practical value in a labor market program evaluation.
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.