ArticleWiley interdisciplinary reviews. Computational statistics
Data Integration in Causal Inference.
Article in Wiley interdisciplinary reviews. Computational statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Considerations for the Integration of Clinical Trials and Real-World Data.Clinical pharmacology and therapeutics · 2026Review
- Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.Current environmental health reports · 2026Review
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data.Annals of statistics · 2026Article
- AI-derived constrained conditional model for screening marker genes through integrated high-throughput transcriptome big data.BMC medical research methodology · 2026Article
- A novel average treatment effect estimation approach for integrated incomplete datasets, with application in childhood anemia.BMC medical research methodology · 2026Article
- Transfer learning-based two-sample Mendelian randomization method for heterogeneous population.Briefings in bioinformatics · 2026Article
- What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research.Statistics in medicine · 2025Article
- Integrating Misclassified EHR Outcomes With Validated Outcomes From a Non-Probability Sample.Statistics in medicine · 2025Article
- Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials.The international journal of biostatistics · 2025Article
- Data integration methods for micro-randomized trials.Biometrics · 2025Article
- Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates.Statistics in biosciences · 2024Article
- The challenges of integrating diverse data sources: A case study in major depression.Health services & outcomes research methodology · 2024Article
- Article
- Social connection as a critical factor for mental and physical health: evidence, trends, challenges, and future implications.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK.The annals of applied statistics · 2024Article
- Mediation analysis using incomplete information from publicly available data sources.Statistics in medicine · 2024Article
- Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies.Statistics in medicine · 2024Article
- Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology.Biometrics · 2024Article
- Covariate adjustment in Bayesian adaptive randomized controlled trials.Statistical methods in medical research · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trial with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.
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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.