Incremental Causal Effect for Time to Treatment Initialization

TL;DR

This paper introduces a method to estimate the causal effect of the timing of treatment initiation in various health and industry contexts, without relying on the positivity assumption, using inverse probability weighting.

Contribution

It develops an identification strategy for incremental causal effects of treatment timing that relaxes the positivity assumption and provides an estimation framework.

Findings

Successfully applied to simulate data demonstrating the method's validity.

Applied to rheumatoid arthritis data to assess methotrexate timing effects.

Shows the approach can handle real-world treatment timing scenarios.

Abstract

We consider time to treatment initialization. This can commonly occur in preventive medicine, such as disease screening and vaccination; it can also occur with non-fatal health conditions such as HIV infection without the onset of AIDS; or in tech industry where items wait to be reviewed manually as abusive or not, etc. While traditional causal inference focused on `when to treat' and its effects, including their possible dependence on subject characteristics, we consider the incremental causal effect when the intensity of time to treatment initialization is intervened upon. We provide identification of the incremental causal effect without the commonly required positivity assumption, as well as an estimation framework using inverse probability weighting. We illustrate our approach via simulation, and apply it to a rheumatoid arthritis study to evaluate the incremental effect of time to start methotrexate on joint pain.