Counterfactual inference enables clinicians to ask "what if" questions about patient outcomes, but standard methods assume feature independence and simultaneous modifiability -- assumptions violated by longitudinal clinical data. We introduce the Sequential Counterfactual Framework, which respects temporal dependencies in electronic health records by distinguishing immutable features (chronic diagnoses) from controllable features (lab values) and modeling how interventions propagate through time. Applied to 2,723 COVID-19 patients (383 Long COVID heart failure cases, 2,340 matched controls), we demonstrate that 38-67% of patients with chronic conditions would require biologically impossible counterfactuals under naive methods. We identify a cardiorenal cascade (CKD -> AKI -> HF) with relative risks of 2.27 and 1.19 at each step, illustrating temporal propagation that sequential -- but not naive -- counterfactuals can capture. Our framework transforms counterfactual explanation from "what if this feature were different?" to "what if we had intervened earlier, and how would that propagate forward?" -- yielding clinically actionable insights grounded in biological plausibility.

翻译：反事实推理使临床医生能够对患者结果提出"假设"性问题，但标准方法假设特征独立且可同时修改——这些假设在纵向临床数据中并不成立。我们提出序贯反事实框架，该框架通过区分不可变特征（慢性诊断）与可控特征（实验室数值），并建模干预措施随时间传播的方式，从而尊重电子健康记录中的时序依赖性。应用于2,723名COVID-19患者（383例长新冠心力衰竭病例，2,340名匹配对照）的数据表明，在简单方法下，38-67%的慢性病患者将需要生物学上不可能的反事实条件。我们识别出心肾级联反应（CKD -> AKI -> HF），其每个步骤的相对风险分别为2.27和1.19，这说明了序贯反事实（而非简单反事实）能够捕捉的时序传播效应。我们的框架将反事实解释从"若该特征不同会怎样？"转变为"若我们更早干预会怎样，这种干预将如何向前传播？"——从而产生基于生物学合理性的临床可操作见解。