arXiv 13 Dec 2017 · Statistics — Machine Learning · publishedBiometrika (2020) · 94 citations (OpenAlex)
arXiv:1712.04912 · PDF · DOI · OpenAlex · Extracted main text
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. Our approach has several advantages over existing methods. From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neural networks, or boosting; moreover, these methods can be fine-tuned by cross validation. Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property: Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same error bounds as an oracle who has a priori knowledge of these two nuisance components. We implement variants of our approach based on penalized regression, kernel ridge regression, and boosting in a variety of simulation setups, and find promising performance relative to existing baselines.
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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 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 9 | 5 | 100% |
| 2 | S. R. Künzel, J. S. Sekhon, P. J. Bickel, and B. Yu (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 1.000 | 9 | 5 | 100% |
| 3 | A. R. Luedtke and M. J. van der Laan (2016) Super-learning of an optimal dynamic treatment rule | 1.000 | 6 | 4 | 100% |
| 4 | S. Powers, J. Qian, K. Jung, A. Schuler, N. H. Shah, T. Hastie, and… (2018) Some methods for heterogeneous treatment effect estimation in high dimensions | 1.000 | 5 | 4 | 100% |
| 5 | K. Imai and M. Ratkovic (2013) Estimating treatment effect heterogeneity in randomized program evaluation | 1.000 | 5 | 3 | 100% |
| 6 | S. Wager and S. Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.928 | 4 | 4 | 100% |
| 7 | S. Athey and G. Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.928 | 4 | 3 | 100% |
| 8 | S. Athey, J. Tibshirani, and S. Wager (2019) Generalized random forests | 0.928 | 4 | 3 | 100% |
| 9 | P. R. Hahn, J. S. Murray, and C. M. Carvalho (2020) Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects | 0.928 | 4 | 3 | 100% |
| 10 | A. Schick (1986) On asymptotically efficient estimation in semiparametric models | 0.928 | 4 | 3 | 100% |
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