Modified Causal Forests for Estimating Heterogeneous Causal Effects

66 Pages Posted: 11 Jan 2019

See all articles by Michael Lechner

Michael Lechner

University of St. Gallen - Swiss Institute for Empirical Economic Research

Date Written: January 2019

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). 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.

Keywords: average treatment effects, causal forests, Causal machine learning, conditional aver-age treatment effects, multiple treatments, selection-on-observable, statistical learning

JEL Classification: C21, J68

Suggested Citation

Lechner, Michael, Modified Causal Forests for Estimating Heterogeneous Causal Effects (January 2019). CEPR Discussion Paper No. DP13430, Available at SSRN: https://ssrn.com/abstract=3314050

Michael Lechner (Contact Author)

University of St. Gallen - Swiss Institute for Empirical Economic Research ( email )

Varnbuelstrasse 14
St. Gallen, 9000
Switzerland
+41 71 224 2320 (Phone)

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
0
Abstract Views
344
PlumX Metrics