arXiv Machine Learning By Amirhossein Zare, Amirhessam Zare, Herlock Rahimi, Reza Salarikia, Mohammad Kashkooli

Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction

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arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.

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arXiv Machine Learning
Jul 31

DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

arXiv:2607. 27263v1 Announce Type: new Abstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science.

By Dennis Thumm, Billy Tim Anthony, Ying Chen
arXiv Machine Learning
Jul 31

Psych-ECA: A Reproducible Semi-Synthetic Benchmark for Synthetic Control Arms in Longitudinal Psychiatry

arXiv:2607. 27224v1 Announce Type: cross Abstract: External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties regulators care about: not only how accurately a method reconstructs untreated trajectories, but whether its uncertainty is calibrated, whether it is robust to the informative observation times common in mental-health records (sicker patients are seen more often), and what false-positive rate it induces in go/no-go trial decisions.

By Aakash Bhagat, Shashank Choudhary