Identification and estimation of treatment and interference effects in observational studies on networks

TL;DR

This paper develops methods to identify and estimate treatment and interference effects in network-based observational studies, addressing bias and unconfoundedness assumptions with new covariate-adjustment techniques.

Contribution

It introduces new estimands for treatment and interference effects, derives bias expressions, and proposes extended unconfoundedness assumptions with generalized propensity scores for valid estimation.

Findings

Bias of naive estimators depends on interference level and treatment association.

Extended unconfoundedness assumption improves causal effect identification.

Simulation results demonstrate effectiveness in realistic network settings.

Abstract

Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of local interference, for instance, potential outcomes of a unit depend on its treatment as well as on the treatments of other local units, such as its neighbors according to the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended - say, to include the treatment of neighbors, and indi- vidual and neighborhood covariates - to guarantee identification and valid inference. Here, we propose new estimands that define treatment and interference effects. We then derive analytical expressions for the bias of a naive estimator that wrongly assumes away interference. The bias depends on the level of interference but also on the degree of association between individual and neighborhood treatments. We propose an extended unconfoundedness assumption that accounts for interference, and we develop new covariate-adjustment methods that lead to valid estimates of treatment and interference effects in observational studies on networks. Estimation is based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. We carry out simulations, calibrated using friendship networks and covariates in a nationally representative longitudinal study of adolescents in grades 7-12, in the United States, to explore finite-sample performance in different realistic settings.