ArticleJournal of the American Statistical Association2020
Combining Multiple Observational Data Sources to Estimate Causal Effects.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-inference framework to integrate multiple external models into a current study.Biostatistics (Oxford, England) · 2023Pooled it
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- Deconfounded-Debiased Estimation and Inference for High-Dimensional Mediation Analysis With Pervasive Hidden Confounders.Statistics in medicine · 2026Article
- Fusing Specialized Surveys of Rare Populations to Larger Surveys for Generalized Inference: Cross-Sectional Survey Study.Journal of medical Internet research · 2026Article
- A novel average treatment effect estimation approach for integrated incomplete datasets, with application in childhood anemia.BMC medical research methodology · 2026Article
- Improving estimation efficiencies for family-based GWAS by integrating large external data.medRxiv : the preprint server for health sciences · 2025Article
- AAdvances in neural information processing systems · 2025Article
- Data fusion methods for the heterogeneity of treatment effect and confounding function.Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability · 2025Article
- Flexible and efficient estimation of causal effects with error-prone exposures: a control variates approach for measurement error.Biometrics · 2025Article
- The Effect of Alcohol Intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data.Journal of the American Statistical Association · 2025Article
- Deconfounded and debiased estimation for high-dimensional linear regression under hidden confounding with application to omics data.Bioinformatics (Oxford, England) · 2025Article
- Integrating Misclassified EHR Outcomes With Validated Outcomes From a Non-Probability Sample.Statistics in medicine · 2025Article
- Task-Agnostic Machine-Learning-Assisted Inference.Advances in neural information processing systems · 2024Article
- Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts.Epidemiology (Cambridge, Mass.) · 2024Article
- INTEGRATING MENDELIAN RANDOMIZATION WITH CAUSAL MEDIATION ANALYSES FOR CHARACTERIZING DIRECT AND INDIRECT EXPOSURE-TO-OUTCOME EFFECTS.The annals of applied statistics · 2024Article
- Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies.Statistics in medicine · 2024Article
- INTEGRATING INCOMPLETE DATA FOR MEDIATION ANALYSIS.Statistica Sinica · 2024Article
- Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.Statistical science : a review journal of the Institute of Mathematical Statistics · 2024Article
- A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.Biometrics · 2023Article
- RWD-INTEGRATED RANDOMIZED CLINICAL TRIAL ANALYSIS.Biopharmaceutical report · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with supplementary information on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. Our framework applies to asymptotically normal estimators, including the commonly used regression imputation, weighting, and matching estimators, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables. We also propose appropriate bootstrap procedures, which makes our method straightforward to implement using software routines for existing estimators. Supplementary materials for this article are available online.
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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.