Causal Modeling of Policy Interventions From Sequences of Treatments and Outcomes

TL;DR

This paper introduces a novel continuous-time model combining Gaussian processes and point processes to estimate and predict the effects of treatment policies on outcomes, enabling counterfactual analysis from observational data.

Contribution

It presents a new joint modeling approach for treatments and outcomes that handles stochastic policies and allows counterfactual predictions, surpassing existing fixed-sequence methods.

Findings

More accurate causal queries on blood glucose data

Effective estimation of treatment policies from observational data

Improved prediction of outcome progression under interventions

Abstract

A treatment policy defines when and what treatments are applied to affect some outcome of interest. Data-driven decision-making requires the ability to predict what happens if a policy is changed. Existing methods that predict how the outcome evolves under different scenarios assume that the tentative sequences of future treatments are fixed in advance, while in practice the treatments are determined stochastically by a policy and may depend, for example, on the efficiency of previous treatments. Therefore, the current methods are not applicable if the treatment policy is unknown or a counterfactual analysis is needed. To handle these limitations, we model the treatments and outcomes jointly in continuous time, by combining Gaussian processes and point processes. Our model enables the estimation of a treatment policy from observational sequences of treatments and outcomes, and it can predict the interventional and counterfactual progression of the outcome after an intervention on the treatment policy (in contrast with the causal effect of a single treatment). We show with real-world and semi-synthetic data on blood glucose progression that our method can answer causal queries more accurately than existing alternatives.