DP13430 Modified Causal Forests for Estimating Heterogeneous Causal Effects

Author(s): Michael Lechner
Publication Date: January 2019
Keyword(s): average treatment effects, causal forests, Causal machine learning, conditional aver-age treatment effects, multiple treatments, selection-on-observable, statistical learning
JEL(s): C21, J68
Programme Areas: Labour Economics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=13430

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). The new esti-mators have desirable theoretical and computational properties for various aggregation levels of the causal effects. An Empirical Monte Carlo study shows that they may outperform previously suggested estimators. Inference tends to be accurate for effects relating to larger groups and conservative for effects relating to fine levels of granularity. An application to the evaluation of an active labour mar-ket programme shows the value of the new methods for applied research.