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

2025/07/09 by Piskorz, Julianna, Krzysztof Kacprzyk, Kacprzyk, Krzysztof +4
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2507.07271

openalex publication_date 2025/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs
• ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
• Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing
• Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
• Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
• On Inductive Biases for Heterogeneous Treatment Effect Estimation
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• Towards optimal doubly robust estimation of heterogeneous causal effects
• Neural Ordinary Differential Equations
• European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
• Bayesian Nonparametric Modeling for Causal Inference
• A Theorem on the Manipulability of Redundant Serial Kinematic Chains

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