Modified Causal Forests for Estimating Heterogeneous Causal Effects

Arbeitspapier

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 frame-work by modifying the Causal Forest approach suggested by Wager and Athey (2018). The new estimators 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 market programme shows the value of the new methods for applied research.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 12040

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Mobility, Unemployment, and Vacancies: Public Policy
• Subject: causal machine learning ; statistical learning ; average treatment effects ; conditional average treatment effects ; multiple treatments ; selection-on-observable ; causal forests

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lechner, Michael
• Event: Veröffentlichung
• (who): Institute of Labor Economics (IZA)
• (where): Bonn
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/193334
• Last update: 10.03.2025, 11:43 AM CET