Primary Submission Category: Continuous Interventions
Causal Inference with Continuous Multiple Time Point Interventions
Authors: Michael Schomaker, Iván Diaz, Paolo Denti, Helen McIlleron,
Presenting Author: Michael Schomaker*
Currently, there are limited options to estimate the effect of variables that are continuous and measured at multiple time points on outcomes, i.e. through the dose-response curve. However, these situations may be of relevance: in pharmacology, one may be interested in how outcomes of people living with -and treated for- HIV, such as viral failure, would vary for time-varying interventions such as different drug concentration trajectories. A challenge for doing causal inference with continuous interventions is that the positivity assumption is typically violated. To address positivity violations, we develop projection functions, which reweigh and redefine the estimand of interest based on functions of the conditional support for the respective interventions. With these functions, we obtain the desired dose-response curve in areas of enough support, and otherwise a meaningful estimand that does not require the positivity assumption. We develop g-computation type plug-in estimators for this case. Those are contrasted with using g-computation estimators in a naïve manner, i.e. applying them to continuous interventions without addressing positivity violations. The ideas are illustrated with longitudinal data from HIV+ children treated with an efavirenz-based regimen. Simulations show in which situations a naïve g-computation approach is appropriate, and in which it leads to bias and how the proposed weighted estimation approach recovers the alternative estimand of interest.