# Confidence Interval Construction for Causally Generalized Estimates With Target Sample Summary Information

## TL;DR

This paper introduces a new method to build confidence intervals for causal estimates when only summary data from a target population is available.

## Contribution

A resampling-based perturbation method is proposed for constructing confidence intervals using only summary-level target data.

## Key findings

- The method effectively constructs confidence intervals for the target ATE with only summary-level data.
- Simulation and real data experiments validate the approach's performance under covariate shift.
- The method improves statistical inference when individual-level data is not accessible.

## Abstract

Generalizing causal findings, such as the average treatment effect (ATE), from a source to a target population is a critical topic in biomedical research. Differences in the distributions of treatment effect modifiers between these populations, known as covariate shift, can lead to varying ATEs. Chen et al. [1] introduced a weighting method to estimate the target ATE using only summary‐level information from a target sample while accounting for the possible covariate shifts. However, the asymptotic variance of the estimate was shown to depend on individual‐level data from the target sample, hindering statistical inference. In this article, we propose a resampling‐based perturbation method for confidence interval construction for the estimated target ATE, utilizing additional summary‐level information. We demonstrate the effectiveness of our approach through simulation and real data settings when only summary‐level information is available.