Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time

TL;DR

This paper introduces a novel framework for modeling treatment effects as smooth, interpretable surfaces over dose and time, capturing dynamic effects beyond the average treatment effect in healthcare applications.

Contribution

It adapts SemanticODE for causal trajectory modeling, enabling interpretable, editable, and robust treatment effect surface estimation over dose and time.

Findings

Accurately models treatment effect trajectories as smooth surfaces.

Provides clinically relevant insights like onset time and peak effect.

Supports domain-informed priors and post-hoc editing for interpretability.

Abstract

The Average Treatment Effect (ATE) is a foundational metric in causal inference, widely used to assess intervention efficacy in randomized controlled trials (RCTs). However, in many applications -- particularly in healthcare -- this static summary fails to capture the nuanced dynamics of treatment effects that vary with both dose and time. We propose a framework for modelling treatment effect trajectories as smooth surfaces over dose and time, enabling the extraction of clinically actionable insights such as onset time, peak effect, and duration of benefit. To ensure interpretability, robustness, and verifiability -- key requirements in high-stakes domains -- we adapt SemanticODE, a recent framework for interpretable trajectory modelling, to the causal setting where treatment effects are never directly observed. Our approach decouples the estimation of trajectory shape from the specification of clinically relevant properties (e.g., maxima, inflection points), supporting domain-informed priors, post-hoc editing, and transparent analysis. We show that our method yields accurate, interpretable, and editable models of treatment dynamics, facilitating both rigorous causal analysis and practical decision-making.