Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
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FactoryBench reveals that frontier LLMs achieve under 50% on structured causal questions and under 18% on decision-making in industrial robotic telemetry.
Digital twins from outcome models trained on historical data can function as robust synthetic controls in single-arm trials, supported by doubly robust estimators, power formulas, and reanalyses in ALS and Huntington's disease.
MEA delivers corrected individual treatment effects, combined effects for any variant combination, and conditional effects given other experiments' states, without requiring factorial designs or traffic restrictions.
citing papers explorer
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Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection
Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
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FactoryBench: Evaluating Industrial Machine Understanding
FactoryBench reveals that frontier LLMs achieve under 50% on structured causal questions and under 18% on decision-making in industrial robotic telemetry.
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Digital Twins as Synthetic Controls in Single-Arm Trials
Digital twins from outcome models trained on historical data can function as robust synthetic controls in single-arm trials, supported by doubly robust estimators, power formulas, and reanalyses in ALS and Huntington's disease.
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Multi-Experiment Analysis
MEA delivers corrected individual treatment effects, combined effects for any variant combination, and conditional effects given other experiments' states, without requiring factorial designs or traffic restrictions.