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[2602.21168] Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma
Summary
This article presents a novel Sequential Counterfactual Framework for analyzing temporal clinical data, addressing limitations of traditional methods in counterfactual inference.
Why It Matters
The framework enhances the understanding of patient outcomes by incorporating temporal dependencies in electronic health records, which is crucial for improving clinical decision-making and patient care, especially in complex cases like Long COVID.
Key Takeaways
- Introduces a Sequential Counterfactual Framework that respects temporal dependencies in clinical data.
- Demonstrates that naive counterfactual methods can lead to biologically implausible outcomes.
- Identifies a cardiorenal cascade in COVID-19 patients, highlighting the importance of sequential analysis.
- Transforms counterfactual questions from static feature changes to dynamic intervention impacts.
- Provides clinically actionable insights that can improve patient management strategies.
Computer Science > Machine Learning arXiv:2602.21168 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 24 Feb 2026] Title:Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma Authors:Jingya Cheng, Alaleh Azhir, Jiazi Tian, Hossein Estiri View a PDF of the paper titled Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma, by Jingya Cheng and 3 other authors View PDF HTML (experimental) Abstract: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 met...
