Zhaomeng Chen, Junting Duan, Victor Chernozhukov, Vasilis Syrgkanis
arXiv 10 Dec 2024 · Statistics — Methodology
arXiv:2412.07184 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes the automatic Doubly Robust Random Forest (DRRF) algorithm for estimating the conditional expectation of a moment functional in the presence of high-dimensional nuisance functions. DRRF extends the automatic debiasing framework based on the Riesz representer to the conditional setting and enables nonparametric, forest-based estimation (Athey et al., 2019; Oprescu et al., 2019). In contrast to existing methods, DRRF does not require prior knowledge of the form of the debiasing term or impose restrictive parametric or semi-parametric assumptions on the target quantity. Additionally, it is computationally efficient in making predictions at multiple query points. We establish consistency and asymptotic normality results for the DRRF estimator under general assumptions, allowing for the construction of valid confidence intervals. Through extensive simulations in heterogeneous treatment effect (HTE) estimation, we demonstrate the superior performance of DRRF over benchmark approaches in terms of estimation accuracy, robustness, and computational efficiency.
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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 | Susan Athey, Julie Tibshirani, and Stefan Wager (2019) Generalized random forests | 1.000 | 5 | 4 | 100% |
| 2 | Miruna Oprescu, Vasilis Syrgkanis, and Zhiwei Steven Wu (2019) Orthogonal random forest for causal inference self | 0.944 | 19 | 8 | 84% |
| 3 | Stefan Wager and Susan Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.737 | 5 | 3 | 40% |
| 4 | Victor Chernozhukov, Whitney K Newey, and Rahul Singh (2022) Automatic debiased machine learning of causal and structural effects self | 0.644 | 2 | 2 | 100% |
| 5 | Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, M… (2019) EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation self | 0.644 | 2 | 2 | 100% |
| 6 | Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whit… Locally robust semiparametric estimation self | 0.511 | 2 | 1 | 100% |
| 7 | Victor Chernozhukov, Whitney Newey, Victor M Quintas-Martinez, and V… (2022) Riesznet and forestriesz: Automatic debiased machine learning with neural nets and random forests self | 0.511 | 2 | 1 | 100% |
| 8 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2017) Double/debiased/neyman machine learning of treatment effects self | 0.511 | 2 | 1 | 100% |
| 9 | Abhinav Kumar, Amit Deshpande, and Amit Sharma (2024) Causal effect regularization: automated detection and removal of spurious correlations | 0.405 | 1 | 1 | 100% |
| 10 | Victor Chernozhukov, Matt Goldman, Vira Semenova, and Matt Taddy Orthogonal machine learning for demand estimation: High dimensional causal inference in dynamic panels self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 17 scored citations.