A Graphical Approach to Treatment Effect Estimation with Observational Network Data
Abstract
We propose an easy-to-use adjustment estimator for the effect of a treatment based on observational data from a single (social) network of units. The approach allows for interactions among units within the network, called interference, and for observed confounding. We define a simplified causal graph that does not differentiate between units, called generic graph. Using valid adjustment sets determined in the generic graph, we can identify the treatment effect and build a corresponding estimator. We establish the estimator's consistency and its convergence to a Gaussian limiting distribution at the parametric rate under certain regularity conditions that restrict the growth of dependencies among units. We empirically verify the theoretical properties of our estimator through a simulation study and apply it to estimate the effect of a strict facial-mask policy on the spread of COVID-19 in Switzerland.
Cite
@article{arxiv.2312.02717,
title = {A Graphical Approach to Treatment Effect Estimation with Observational Network Data},
author = {Meta-Lina Spohn and Leonard Henckel and Marloes H. Maathuis},
journal= {arXiv preprint arXiv:2312.02717},
year = {2023}
}