Estimating Optimal Treatment Rules with an Instrumental Variable: A Partial Identification Learning Approach

Understand

Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions.

• One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject's covariate information, which is often challenging in observational studies due to the universal concern of unmeasured confounding.
• Instrumental variables (IVs) are widely-used tools to infer the treatment effect when there is unmeasured confounding between the treatment and outcome.
• In this work, we propose a general framework of approaching the optimal ITR estimation problem when a valid IV is allowed to only partially identify the treatment effect.