Combining Multiple Observational Data Sources to Estimate Causal Effects.

TitleCombining Multiple Observational Data Sources to Estimate Causal Effects.
Publication TypeJournal Article
Year of Publication2020
AuthorsYang, Shu, and Peng Ding
JournalJ Am Stat Assoc
Date Published2020

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.

Alternate JournalJ Am Stat Assoc
Original PublicationCombining multiple observational data sources to estimate causal effects.
PubMed ID33088006
PubMed Central IDPMC7571608
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States