ArticleAnnals of statistics2024
Minimax rates for heterogeneous causal effect estimation.
Article in Annals of statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Heterogeneity in associations between food insecurity and diabetes outcomes.Journal of epidemiology and community health · 2025Article
- Data fusion methods for the heterogeneity of treatment effect and confounding function.Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability · 2025Article
- Doubly robust machine learning-based estimation methods for instrumental variables with an application to surgical care for cholecystitis.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- One-Step Estimation of Differentiable Hilbert-Valued Parameters.Annals of statistics · 2024Article
- Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators.Journal of econometrics · 2024Article
- Causal isotonic calibration for heterogeneous treatment effects.Proceedings of machine learning research · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Estimation of heterogeneous causal effects - i.e., how effects of policies and treatments vary across subjects - is a fundamental task in causal inference. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years, but questions surrounding optimality have remained largely unanswered. In particular, a minimax theory of optimality has yet to be developed, with the minimax rate of convergence and construction of rate-optimal estimators remaining open problems. In this paper we derive the minimax rate for CATE estimation, in a Hölder-smooth nonparametric model, and present a new local polynomial estimator, giving high-level conditions under which it is minimax optimal. Our minimax lower bound is derived via a localized version of the method of fuzzy hypotheses, combining lower bound constructions for nonparametric regression and functional estimation. Our proposed estimator can be viewed as a local polynomial R-Learner, based on a localized modification of higher-order influence function methods. The minimax rate we find exhibits several interesting features, including a non-standard elbow phenomenon and an unusual interpolation between nonparametric regression and functional estimation rates. The latter quantifies how the CATE, as an estimand, can be viewed as a regression/functional hybrid.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.