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CATE meets ML: Conditional average treatment effect and machine learning

Daniel Jacob

No 2021-005, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are flip-sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory allows us to estimate not only the average but a personalized treatment effect - the conditional average treatment effect (CATE). In this tutorial, we give an overview of novel methods, explain them in detail, and apply them via Quantlets in real data applications. We study the effect that microcredit availability has on the amount of money borrowed and if the 401(k) pension plan eligibility has an impact on net financial assets, as two empirical examples. The presented toolbox of methods contains metalearners, like the Doubly-Robust, the R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized random forest. In both, the microcredit and the 401(k) example, we find a positive treatment effect for all observations but diverse evidence of treatment effect heterogeneity. An additional simulation study, where the true treatment effect is known, allows us to compare the different methods and to observe patterns and similarities.

Keywords: Causal Inference; CATE; Machine Learning; Tutorial (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2021005

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