Estimating causal effects of continuous-time dynamic treatments with unmeasured confounders

TL;DR

This paper introduces a Bayesian method to estimate causal effects of continuous-time dynamic treatments with unmeasured confounders, demonstrated through simulations and application to oxytocin use in postpartum hemorrhage.

Contribution

It develops a novel approach to causal inference in continuous-time settings accounting for unmeasured confounding, extending existing frameworks.

Findings

The method provides approximately unbiased estimates in simulations.

Application to oxytocin treatment reveals meaningful causal insights.

Compared to existing methods, it performs better under confounding.

Abstract

Modern medical research demands specialized causal inference methods evaluating complex continuous-time dynamic treatment regimens using observational data. For instance, obtaining the causal effects of intravenous administration, a continuous process involving dynamic adjustments of the treatment dose, can guide clinicians on drug use. However, the existing causal inference frameworks in longitudinal studies typically assume that time advances in discrete time steps. Therefore, this paper proposes a new methodology to estimate the causal effects of continuous-time dynamic treatments in the presence of unmeasured confounding. Unmeasured confounding is incorporated into estimating continuous-time Marginal Structural Models from a Bayesian perspective. Simulation demonstrates that compared to existing methods, the proposed approach can provide approximately unbiased estimates for target causal parameters across three degrees of confounding. The proposed method is applied to analyze the causal relationship between the intravenous oxytocin administration process and postpartum hemorrhage, leading to meaningful results that may guide clinicians in using oxytocin.