ArticleStatistics in medicine2021
A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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Who cites it
15 citing papers in PubMed, 4 citations in OpenAlex.
- Trial
- Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.Current environmental health reports · 2026Review
- Regression augmented weighting adjustment for indirect comparisons in health decision modelling.Research synthesis methods · 2025Article
- Accuracy of preferred language data in a multi-hospital electronic health record in Toronto, Canada.PLOS digital health · 2025Article
- Modern statistical techniques for cardiothoracic surgeons: Part 8-Bayesian analysis and beyond.Indian journal of thoracic and cardiovascular surgery · 2025Article
- A causal Bayesian model to evaluate shoulder pathology effect on glenoid bone mineral density.Journal of orthopaedic surgery and research · 2025Article
- causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes.Observational studies · 2025Article
- Priors and Propensity Scores in Bayesian Causal Inference.Observational studies · 2025Article
- Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates.Statistics in biosciences · 2024Article
- Bridging theory and data: A computational workflow for cultural evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Covariate adjustment in Bayesian adaptive randomized controlled trials.Statistical methods in medical research · 2024Article
- Socio-economic and ethnic disparities in childhood cancer survival, Yorkshire, UK.British journal of cancer · 2023Article
- Oxygenation thresholds for invasive ventilation in hypoxemic respiratory failure: a target trial emulation in two cohorts.Critical care (London, England) · 2023Article
- An introduction to causal inference for pharmacometricians.CPT: pharmacometrics & systems pharmacology · 2023Article
- Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Substantial advances in Bayesian methods for causal inference have been made in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demonstrate how priors can induce shrinkage and sparsity in parametric models and be used to perform probabilistic sensitivity analyses around causal assumptions. We provide an overview of nonparametric Bayesian estimation and survey their applications in the causal inference literature. Inference in the point-treatment and time-varying treatment settings are considered. For the latter, we explore both static and dynamic treatment regimes. Throughout, we illustrate implementation using off-the-shelf open source software. We hope to leave the reader with implementation-level knowledge of Bayesian causal inference using both parametric and nonparametric models. All synthetic examples and code used in the paper are publicly available on a companion GitHub repository.
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What OpenQuestion holds
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.