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Reliable Decision Support using Counterfactual Models

NeurIPS 2017 · Reinforcement learning › Policy gradient and actor-critic · approach: Tree search and policy optimization · 86 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the counterfactual objective instead of the standard supervised objectiveGP risk model · Simulated risk prediction, Regime BRisk Score Δ from A: 0.083 -> 0.001Helped
Use the counterfactual objective instead of the standard supervised objectiveGP risk model · Simulated risk prediction, Regime BKendall's τ from A: 0.857 -> 0.998Helped
Use the counterfactual objective instead of the standard supervised objectiveGP risk model · Simulated risk prediction, Regime BAUC: 0.832 -> 0.872Helped
Train under a policy that violates the CGP assumptionsCGP · Simulated risk prediction, Regime CRisk Score Δ from A: 0.001 -> 0.128Hurt
Train under a policy that violates the CGP assumptionsCGP · Simulated risk prediction, Regime CKendall's τ from A: 0.998 -> 0.562Hurt
Train under a policy that violates the CGP assumptionsCGP · Simulated risk prediction, Regime CAUC: 0.872 -> 0.829Hurt
Train on a different action policyBaseline GP · Simulated risk prediction, Regime BRisk Score Δ from A: 0.000 -> 0.083Hurt
Train on a different action policyBaseline GP · Simulated risk prediction, Regime BAUC: 0.853 -> 0.832Hurt
Add treatment-effect modeling to the three-GP mixture (ablation)CGP outcome model · MIMIC-II creatinine, 24-hour horizonMAE: 0.07 (0.06, 0.08)Helped
Add treatment-effect modeling to the three-GP mixture (ablation)CGP outcome model · MIMIC-II creatinine, 24-48 hoursMAE: 0.09 (0.08, 0.10)Helped
Use a mixture of GPs instead of a single GP while modeling treatment effects (ablation)CGP outcome model · MIMIC-II creatinine, 24-hour horizonMAE: 0.04 (0.04, 0.05)Helped
Use a mixture of GPs instead of a single GP while modeling treatment effects (ablation)CGP outcome model · MIMIC-II creatinine, 24-48 hoursMAE: 0.03 (0.02, 0.04)Helped

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