ArticleProceedings of the National Academy of Sciences of the United States of America2016
Causal inference and the data-fusion problem.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 148 papers, 3 of them syntheses that pooled it.
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
148 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Pooled it
- Cortisol Co-Secretion and Clinical Usefulness of ACTH Stimulation Test in Primary Aldosteronism: A Systematic Review and Biases in Epidemiological Studies.Frontiers in endocrinology · 2021Pooled it
- Statistical tests and identifiability conditions for pooling and analyzing multisite datasets.Proceedings of the National Academy of Sciences of the United States of America · 2018Pooled it
- Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization Due to Infant Respiratory Illness.American journal of epidemiology · 2023Trial
- Estimating the impact of stimulant use on initiation of buprenorphine and extended-release naltrexone in two clinical trials and real-world populations.Addiction science & clinical practice · 2023Trial
- Impact of risk factor burden and vascular bed involvement on dementia risk in individuals with established cardiovascular disease.American journal of preventive cardiology · 2026Article
- Combining Observational Studies to Reduce Multiple Biases.Epidemiology (Cambridge, Mass.) · 2026Article
- Clinical predictive artificial intelligence evaluation: A narrative review of trial designs and practical considerations.PLOS digital health · 2026Review
- Validating medical digital twins for clinical decision support: beyond predictive accuracy.JAMIA open · 2026Review
- Survival as a source of confounding, selection bias, and effect modification.International journal of epidemiology · 2026Article
- Causal inference and digital twins: a roadmap for the future of clinical trials.NPJ digital medicine · 2026Review
- Effect of antihypertensive medication reduction on short-term blood pressure control in older adults: calibration of OPTiMISE trial results to real-world primary care data.Age and ageing · 2026Article
- Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data.Annals of statistics · 2026Article
- Long COVID Persistence and Surveillance Gaps Across 58 US Hospitals.JAMA network open · 2026Article
- Fusing Specialized Surveys of Rare Populations to Larger Surveys for Generalized Inference: Cross-Sectional Survey Study.Journal of medical Internet research · 2026Article
- Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.Biometrics · 2026Article
- Comparing HIV Vaccine Immunogenicity Across Trials With Different Populations and Study Designs.Statistics in medicine · 2026Article
- Developing a general research framework for long COVID using causal modelling.Communications medicine · 2026Article
- Virtual Cells Need Context, Not Just Scale.bioRxiv : the preprint server for biology · 2026Article
- Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials.Statistics in medicine · 2026Article
88 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We review concepts, principles, and tools that unify current approaches to causal analysis and attend to new challenges presented by big data. In particular, we address the problem of data fusion-piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to big data analysts, because the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. We here present a general, nonparametric framework for handling these biases and, ultimately, a theoretical solution to the problem of data fusion in causal inference tasks.
Indexed as
Identifiers
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.