[Causal Inference] Sensitivity Analysis of Causal Effects on Network under Unobserved Confoundings
in Keep calm and do research on Causal inference, Sensitivity analysis, Network interference Last modified at:
This paper introduces the Interference Sensitivity Model (ISM), a novel approach to assess the sensitivity of causal effects in network settings under unobserved confounding and partial interference, which allows for interactions within clusters but not across clusters. The ISM provides credible bounds for Average Direct Effects (ADE) and Average Spillover Effects (ASE) by maximizing a constrained geometric programming, which is a non-convex optimization problem.
