Data integration using covariate summaries from external sources

TL;DR

This paper introduces new data integration methods that use only external summary statistics to improve analysis robustness and extend causal inference capabilities across heterogeneous datasets.

Contribution

The authors develop novel techniques for data integration and causal inference that rely solely on external covariate summaries, avoiding the need for individual-level data.

Findings

Effective estimators constructed from summary statistics

Applicable to both homogeneous and heterogeneous data

Extended framework for causal inference and treatment effect estimation

Abstract

In modern data analysis, information is frequently collected from multiple sources, often leading to challenges such as data heterogeneity and imbalanced sample sizes across datasets. Robust and efficient data integration methods are crucial for improving the generalization and transportability of statistical findings. In this work, we address scenarios where, in addition to having full access to individualized data from a primary source, supplementary covariate information from external sources is also available. While traditional data integration methods typically require individualized covariates from external sources, such requirements can be impractical due to limitations related to accessibility, privacy, storage, and cost. Instead, we propose novel data integration techniques that rely solely on external summary statistics, such as sample means and covariances, to construct robust estimators for the mean outcome under both homogeneous and heterogeneous data settings. Additionally, we extend this framework to causal inference, enabling the estimation of average treatment effects for both generalizability and transportability.