ArticleBiometrics2022
Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
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.
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Who cites it
9 citing papers in PubMed.
- Authors' reply to the Discussion of 'Methods for estimating the exposure-response curve to inform the new safety standards for fine particulate matter'.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- AVERAGED PREDICTION MODELS (APM): IDENTIFYING CAUSAL EFFECTS IN CONTROLLED PRE-POST SETTINGS WITH APPLICATION TO GUN POLICY.The annals of applied statistics · 2025Article
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- Identification and Estimation of Causal Effects Using Non-Concurrent Controls in Platform Trials.Statistics in medicine · 2025Article
- A doubly robust estimator for continuous treatments in high dimensions.BMC medical research methodology · 2025Article
- Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy.Statistical methods in medical research · 2024Article
- Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.Journal of agricultural, biological, and environmental statistics · 2023Article
- A reference-free R-learner for treatment recommendation.Statistical methods in medical research · 2023Article
- Inference under unequal probability sampling with the Bayesian exponentially tilted empirical likelihood.Biometrika · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior distributions of treatment and outcome models can be used together with doubly robust estimators. We propose an approach to uncertainty quantification for the doubly robust estimator, which utilizes posterior distributions of model parameters and (1) results in good frequentist properties in small samples, (2) is based on a single run of a Markov chain Monte Carlo (MCMC) algorithm, and (3) improves over frequentist measures of uncertainty which rely on asymptotic properties. We consider a flexible framework for modeling the treatment and outcome processes within the Bayesian paradigm that reduces model dependence, accommodates nonlinearity, and achieves dimension reduction of the covariate space. We illustrate the ability of the proposed approach to flexibly estimate causal effects in high dimensions and appropriately quantify uncertainty. We show that our proposed variance estimation strategy is consistent when both models are correctly specified, and we see empirically that it performs well in finite samples and under model misspecification. Finally, we estimate the effect of continuous environmental exposures on cholesterol and triglyceride levels.
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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.