arXiv 20 Apr 2021 · Econometrics · 3 citations (OpenAlex)
arXiv:2104.09935 · PDF · DOI · OpenAlex · Extracted main text
For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow 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 401(k) pension plan eligibility has an impact on net financial assets, as two empirical examples. The presented toolbox of methods contains meta-learners, like the Doubly-Robust, 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 401(k) example, we find a positive treatment effect for all observations but conflicting 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.956 | 8 | 3 | 88% |
| 2 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 5 | 2 | 100% |
| 3 | Peter M Robinson (1988) Root-n-consistent semiparametric regression | 0.737 | 3 | 2 | 100% |
| 4 | Susan Athey, Stefan Wager, and Julie Tibshirani (2019) Generalized random forests | 0.644 | 4 | 1 | 100% |
| 5 | P. Richard Hahn, Jared S. Murray, and Carlos M. Carvalho (2020) Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects | 0.644 | 4 | 1 | 100% |
| 6 | Jennifer L Hill (2011) Bayesian nonparametric modeling for causal inference | 0.644 | 4 | 1 | 100% |
| 7 | X Nie and S Wager (2020) Quasi-oracle estimation of heterogeneous treatment effects | 0.644 | 4 | 1 | 100% |
| 8 | Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H… Some methods for heterogeneous treatment effect estimation in high dimensions | 0.644 | 4 | 1 | 100% |
| 9 | Daniel G Horvitz and Donovan J Thompson (1952) A generalization of sampling without replacement from a finite universe | 0.644 | 3 | 2 | 67% |
| 10 | Bruno Crépon, Florencia Devoto, Esther Duflo, and William Parienté (2015) Estimating the impact of microcredit on those who take it up: Evidence from a randomized experiment in morocco | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance | 0.737 | 3 | 2 |
| 2 | Profit-Aligned CATE Estimation: Reconciling Policy Learning and Inference | 0.405 | 1 | 1 |