On the limitations of causal inference with current-treatment Cox models
仅含当前治疗的 Cox 模型在因果推断中的局限AI 解读 ↓
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仅含当前治疗的 Cox 模型在因果推断中的局限
- 研究问题
- 在治疗随时间变化且存在未观测预后异质性时，仅含当前治疗的 Cox 模型，何时能够转换为具有因果含义的干预生存概率？
- 方法与思路
- 作者构建连续时间框架，分析条件与边际 Markov 性同时成立的条件，并用反例检查当前治疗模型向生存尺度转换的有效性。
- 材料陈述的贡献
- 摘要称同时成立的 Markov 性要求加性危险率尺度上不存在治疗与脆弱性交互；反例显示正确拟合模型仍可能不能恢复干预生存概率。
- 阅读关注点
- 核对条件与边际 Markov 性的定义、无交互结论的精确条件，并检查反例如何区分模型拟合正确与干预生存概率识别。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核验；脆弱性未被观测，相关结构假设不可验证，摘要也未给出完整定理范围。
原文摘要
Cox models with time-varying treatments often include only the current treatment level. Translating such a model into causally meaningful intervention-specific survival probabilities relies on the Markov property: that the hazard is independent of treatment history conditional on current treatment. For the Markov property to not be population-specific, it needs to hold also conditional on any unmeasured prognostic heterogeneity (frailty). Using a discrete-time argument, earlier work concluded that the Markov property can hold both conditionally and marginally only in the absence of a treatment effect or an effect of the unmeasured heterogeneity. We broaden this argument by developing a continuous-time framework that encompasses both proposed extensions of the Kaplan-Meier curve and current-treatment marginal structural Cox models, and sharpen it by characterizing precisely the conditions under which the Markov property can hold both conditionally and marginally. Specifically, we show that it requires the absence of treatment-frailty interaction on the additive hazard scale. As frailty is inherently unmeasured, such a no-interaction assumption cannot be verified. Consequently, causal interpretation of a current-treatment marginal structural Cox model rests on a strong and unverifiable structural assumption. Illustrating this point, we construct a setting in which a Cox model is correctly specified conditionally on current treatment only, although the marginal Markov property fails. Transforming the fitted model to the survival scale does not recover the true intervention-specific survival probabilities. Thus, moving from the hazard scale to the survival scale is not in itself sufficient to obtain a causal interpretation in the time-varying treatment setting.