Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions
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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 either neglects the dependence of future treatments on outcomes preceding them or implicitly assumes the treatment policy is known, and hence 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 real-world data on blood glucose progression and a simulation study building on top of this.
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