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Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study

Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study

Causal neural networks (NNs), including TARNet and Dragonnet, learn by sharing input data to estimate both factual and counterfactual outcomes. This approach is currently an active area of research [17-19]. Dragonnet also uses “learned data” to predict propensity scores by tradeoff with prediction quality, which yields better average treatment effect (ATE) estimates [18].

Sermkiat Lolak, John Attia, Gareth J McKay, Ammarin Thakkinstian

JMIR Cardio 2025;9:e50627