Combining observational and experimental data for causal inference considering data privacy

TL;DR

This paper investigates how to combine privacy-preserving observational data with experimental data to improve causal inference, balancing data utility and privacy risks through various data transformation techniques.

Contribution

It introduces methods for using privacy-preserving transformations of observational data to enhance treatment effect estimation when combining with experimental data.

Findings

Transformed observational data can improve treatment effect estimates.

Privacy-utility trade-off depends on the transformation technique.

Leveraging privacy-preserving data enhances causal inference accuracy.

Abstract

Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive information might be tolerable to organizations that house confidential data. In these cases, organizations can employ data privacy techniques, which decrease disclosure risk, potentially at the expense of data utility. In this paper, we explore disclosure limiting transformations of observational data, which can be combined with experimental data to estimate the sample and population average treatment effects. We consider leveraging observational data to improve generalizability of treatment effect estimates when a randomized experiment (RCT) is not representative of the population of interest, and to increase precision of treatment effect estimates. Through simulation studies, we illustrate the trade-off between privacy and utility when employing different disclosure limiting transformations. We find that leveraging transformed observational data in treatment effect estimation can still improve estimation over only using data from an RCT.