Working Paper: CEPR ID: DP13430
Authors: Michael Lechner
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: Causal Machine Learning; Statistical Learning; Average Treatment Effects; Conditional Average Treatment Effects; Multiple Treatments; Selection on Observable; Causal Forests
JEL Codes: C21; J68
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Treatment (C22) | Employment Outcomes (J68) |
| Modified Causal Forest Approach (C22) | Accurate Estimates of Causal Effects (C51) |
| Treatment Probability Heterogeneity (C25) | Estimation of Individualized Average Treatment Effects (IATEs) (C22) |
| Selection Bias (C24) | Accuracy of Estimates (C13) |
| Modified Causal Forest Approach (C22) | Conservative Inference at Finer Levels (C20) |