ArticleAnnals of statistics2022
BOUNDS ON THE CONDITIONAL AND AVERAGE TREATMENT EFFECT WITH UNOBSERVED CONFOUNDING FACTORS.
Article in Annals of statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Best practices for moving from correlation to causation in ecological research.Nature communications · 2026Review
- Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies.Biometrika · 2025Article
- Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Mediation analysis using incomplete information from publicly available data sources.Statistics in medicine · 2024Article
- Sensitivity analysis of individual treatment effects: A robust conformal inference approach.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Sensitivity analysis for causality in observational studies for regulatory science.Journal of clinical and translational science · 2023Article
- BOUNDS ON THE CONDITIONAL AND AVERAGE TREATMENT EFFECT WITH UNOBSERVED CONFOUNDING FACTORS.Annals of statistics · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
For observational studies, we study the sensitivity of causal inference when treatment assignments may depend on unobserved confounders. We develop a loss minimization approach for estimating bounds on the conditional average treatment effect (CATE) when unobserved confounders have a bounded effect on the odds ratio of treatment selection. Our approach is scalable and allows flexible use of model classes in estimation, including nonparametric and black-box machine learning methods. Based on these bounds for the CATE, we propose a sensitivity analysis for the average treatment effect (ATE). Our semiparametric estimator extends/bounds the augmented inverse propensity weighted (AIPW) estimator for the ATE under bounded unobserved confounding. By constructing a Neyman orthogonal score, our estimator of the bound for the ATE is a regular root-
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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.