Joint Point Process Model for Counterfactual Treatment-Outcome Trajectories Under Policy Interventions
Keywords: Causal inference, Policy intervention, Point processes, Gaussian Processes, Non-parametric
Abstract: Policy makers need to predict the progression of an outcome before adopting a new treatment policy, which defines when and how a sequence of treatments affecting the outcome occurs in continuous time. Commonly, algorithms that predict interventional future outcome trajectories take a fixed sequence of future treatments as input. This excludes scenarios where the policy is unknown or a counterfactual analysis is needed. To handle these limitations, we develop a joint model for treatments and outcomes, which allows for the estimation of treatment policies and effects from sequential treatment--outcome data. It can answer interventional and counterfactual queries about interventions on treatment policies, as we show with a realistic semi-synthetic simulation study. This abstract is based on work that is currently under review (Anonymous).