Structural Nested Mean Models Under Parallel Trends with Interference

TL;DR

This paper extends Difference-in-Differences methods to account for interference and spillover effects, allowing for more comprehensive causal inference in settings with network or cluster interactions.

Contribution

It develops a new framework for DiD with Structural Nested Mean Models that incorporates interference, enabling estimation of direct and spillover effects under complex exposure histories.

Findings

Method effectively models spillover effects in simulations.

Application to Medicaid expansion shows significant spillover impacts.

Framework accommodates both cluster and network interference structures.

Abstract

Despite the common occurrence of interference in Difference-in-Differences (DiD) applications, standard DiD methods rely on an assumption that interference is absent, and comparatively little work has considered how to accommodate and learn about spillover effects within a DiD framework. Here, we extend the `DiD-SNMMs' of Shahn et al (2022) to accommodate interference in a time-varying DiD setting. Doing so enables estimation of a richer set of effects than previous DiD approaches. For example, DiD-SNMMs do not assume the absence of spillover effects after direct exposures and can model how effects of direct or indirect (i.e. spillover) exposures depend on past and concurrent (direct or indirect) exposure and covariate history. We consider both cluster and network interference structures and illustrate the methodology in simulations and an application to effects of Medicaid expansion on uninsurance rates.