Causal Inference under Networked Interference and Intervention Policy Enhancement
This work addresses a challenging task in causal inference for fields like economics and social science, where interference biases estimates, but it is incremental as it applies existing GNN methods to this known bottleneck.
The paper tackles the problem of estimating individual treatment effects under network interference, where treatments on one unit affect neighboring units, by using Graph Neural Networks (GNNs) to capture dependencies and deriving causal effect estimators, with results including policy regret bounds and heuristic error bounds for GNN-based estimators.
Estimating individual treatment effects from data of randomized experiments is a critical task in causal inference. The Stable Unit Treatment Value Assumption (SUTVA) is usually made in causal inference. However, interference can introduce bias when the assigned treatment on one unit affects the potential outcomes of the neighboring units. This interference phenomenon is known as spillover effect in economics or peer effect in social science. Usually, in randomized experiments or observational studies with interconnected units, one can only observe treatment responses under interference. Hence, how to estimate the superimposed causal effect and recover the individual treatment effect in the presence of interference becomes a challenging task in causal inference. In this work, we study causal effect estimation under general network interference using GNNs, which are powerful tools for capturing the dependency in the graph. After deriving causal effect estimators, we further study intervention policy improvement on the graph under capacity constraint. We give policy regret bounds under network interference and treatment capacity constraint. Furthermore, a heuristic graph structure-dependent error bound for GNN-based causal estimators is provided.