# Is it who you are or where you are? Accounting for compositional differences in cross-site treatment variation

arXiv: https://arxiv.org/abs/2103.14765 · HTML: https://mlsanity.com/paper/2103.14765/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Data Fusion for High-Resolution Estimation](https://mlsanity.com/paper/2508.14858/) (2025)

## Abstract (arXiv)

Multisite trials, in which treatment is randomized separately in multiple sites, offer a unique opportunity to disentangle treatment effect variation due to "compositional" differences in the distributions of unit-level features from variation due to "contextual" differences in site-level features. In particular, if we can re-weight (or "transport") each site to have a common distribution of unit-level covariates, the remaining effect variation captures contextual differences across sites. In this paper, we develop a framework for transporting effects in multisite trials using approximate balancing weights, where the weights are chosen to directly optimize unit-level covariate balance between each site and the target distribution. We first develop our approach for the general setting of transporting the effect of a single-site trial. We then extend our method to multisite trials, assess its performance via simulation, and use it to analyze a series of multisite trials of welfare-to-work programs. Our method is available in the balancer R package.