Network Analysis and Causal Inference

The field of causal inference develops methods for estimating treatment effects, often relying on the Stable Unit Treatment Value Assumption (SUTVA), which states that a unit's outcome depends only on its own treatment. However, in many real-world settings, SUTVA is violated due to interference—where the treatment assigned to one unit influences the outcomes of others. Such interference can arise from social interactions among units or competition for shared resources, complicating causal analysis and leading to biased estimates. Fortunately, in many cases, interference follows structured patterns that can potentially be leveraged for more accurate estimation. In this paper, we examine and formalize two specific forms of structured interference—monotone interference and submodular interference—which we believe arise in many practical settings. We investigate how incorporating these structures can improve causal effect estimation. Our main contributions are (i) a set of bounds relating key interference estimands under these structural assumptions and (ii) new estimators that integrate these structures through constrained optimization. Since these constraints may introduce bias, we further develop debiasing techniques based on treatment regeneration and bootstrap methods to mitigate this issue.

This is joint work with Kevin Han and Johan Ugander.