Data Integration in Causal Inference

2021/10/03 by Xu Shi, Ziyang Pan, Shi, Xu +3 · 3 citations
Mathematics · Biochemistry, Genetics and Molecular Biology · #Advanced Causal Inference Techniques #Statistical Methods in Clinical Trials #Genetic Associations and Epidemiology

paper · pdf · doi:10.48550/arxiv.2110.01106

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.

Cited by

• Causal Inferences from Digital Behavioral Data
• Collaborative Indirect Treatment Comparisons with Multiple Distributed Single-arm Trials
• The challenges of integrating diverse data sources: A case study in major depression

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