建模治疗效果随剂量和时间变化的动态轨迹，更精准指导临床决策。
Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time
- 用平滑曲面建模治疗效果随剂量与时间的变化
- 可提取起效时间、峰值效应、持续时长等关键临床指标
- 支持医生根据医学知识编辑模型，提升可解释性
平均治疗效应（ATE）是因果推断中的基础指标，广泛用于随机对照试验中评估干预效果。但在医疗等领域，这种静态总结无法捕捉治疗效果随剂量与时间变化的复杂动态。本文提出一种框架，将治疗效应轨迹建模为剂量与时间上的平滑表面，可提取起效时间、峰值效应和受益持续时间等临床可操作信息。为确保可解释性、鲁棒性和可验证性，我们引入语义微分方程（SemanticODE）方法，将其适配至因果设定——治疗效应从未被直接观测。该方法分离轨迹形状估计与临床特征（如极大值点、拐点）的指定，支持领域先验、事后编辑与透明分析。实验表明，该方法能生成准确、可解释且可编辑的治疗动态模型，助力严谨的因果分析与实际医疗决策。
原文摘要 · Abstract (English)
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.
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