Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding
This work addresses a key challenge in network causal inference for researchers, enabling more accurate effect estimation in complex interference settings, though it is incremental by building on existing graphical model frameworks.
The paper tackles the problem of distinguishing between contagion and latent confounding as sources of observed correlations in network studies, deriving likelihood ratio tests for identification and proposing unbiased estimation strategies for network causal effects under full interference. The methods are evaluated with synthetic data and real-world networks, showing effectiveness in scenarios not previously considered.
A key question in many network studies is whether the observed correlations between units are primarily due to contagion or latent confounding. Here, we study this question using a segregated graph (Shpitser, 2015) representation of these mechanisms, and examine how uncertainty about the true underlying mechanism impacts downstream computation of network causal effects, particularly under full interference -- settings where we only have a single realization of a network and each unit may depend on any other unit in the network. Under certain assumptions about asymptotic growth of the network, we derive likelihood ratio tests that can be used to identify whether different sets of variables -- confounders, treatments, and outcomes -- across units exhibit dependence due to contagion or latent confounding. We then propose network causal effect estimation strategies that provide unbiased and consistent estimates if the dependence mechanisms are either known or correctly inferred using our proposed tests. Together, the proposed methods allow network effect estimation in a wider range of full interference scenarios that have not been considered in prior work. We evaluate the effectiveness of our methods with synthetic data and the validity of our assumptions using real-world networks.