Yiyan Huang, Cheuk Hang Leung, Siyi Wang, Yijun Li, Qi Wu
arXiv 28 Feb 2024 · Machine Learning
arXiv:2402.18392 · PDF · DOI · OpenAlex · Extracted main text
The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators have been developed with advancements in machine learning and causal inference. However, selecting the desirable CATE estimator through a conventional model validation procedure remains impractical due to the absence of counterfactual outcomes in observational data. Existing approaches for CATE estimator selection, such as plug-in and pseudo-outcome metrics, face two challenges. First, they must determine the metric form and the underlying machine learning models for fitting nuisance parameters (e.g., outcome function, propensity function, and plug-in learner). Second, they lack a specific focus on selecting a robust CATE estimator. To address these challenges, this paper introduces a Distributionally Robust Metric (DRM) for CATE estimator selection. The proposed DRM is nuisance-free, eliminating the need to fit models for nuisance parameters, and it effectively prioritizes the selection of a distributionally robust CATE estimator. The experimental results validate the effectiveness of the DRM method in selecting CATE estimators that are robust to the distribution shift incurred by covariate shift and hidden confounders.
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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 | Alicia Curth and Mihaela Van Der Schaar (2023) In search of insights, not magic bullets: Towards demystification of the model selection dilemma in heterogeneous treatment effe… | 1.000 | 10 | 4 | 100% |
| 2 | Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, and Vasilis Syrgkanis (2024) Empirical analysis of model selection for heterogenous causal effect estimation | 0.928 | 5 | 4 | 80% |
| 3 | Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah (2018) A comparison of methods for model selection when estimating individual treatment effects | 0.928 | 4 | 3 | 100% |
| 4 | Xinkun Nie and Stefan Wager (2021) Quasi-oracle estimation of heterogeneous treatment effects | 0.843 | 5 | 3 | 60% |
| 5 | Alicia Curth and Mihaela van der Schaar (2021) On inductive biases for heterogeneous treatment effect estimation | 0.811 | 4 | 2 | 100% |
| 6 | Uri Shalit, Fredrik D Johansson, and David Sontag (2017) Estimating individual treatment effect: generalization bounds and algorithms | 0.811 | 4 | 2 | 100% |
| 7 | Ahmed Alaa and Mihaela Van Der Schaar (2019) Validating causal inference models via influence functions | 0.737 | 3 | 3 | 67% |
| 8 | Dylan J Foster and Vasilis Syrgkanis (2023) Orthogonal statistical learning | 0.737 | 3 | 3 | 67% |
| 9 | Edward H Kennedy (2023) Towards optimal doubly robust estimation of heterogeneous causal effects | 0.737 | 3 | 3 | 67% |
| 10 | Donald B Rubin (2005) Causal inference using potential outcomes: Design, modeling, decisions | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 79 scored citations.