WITHDRAWN Causal Inference with Linked Data Files

Music City Center 

Causal analysis of observational studies requires data that comprise a set of covariates, a treatment assignment, and the observed outcomes. However, data confidentiality restrictions may distribute these variables across two or more files. In the absence of unique identifiers to link records across files, probabilistic record linkage algorithms can be leveraged to merge the datasets. Current applications of record linkage are concerned with estimation of associations, and not causal relationships. We present a Bayesian framework for record linkage and causal inference when causally relevant variables are spread across two files. Using simulations, we show that the new framework can improve the linkage accuracy, and provide accurate post-linkage causal inferences.

record linkage

causal inference

missing data 

Government Statistics Section