Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
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Proposes causal reinforcement learning (CRL) as a framework that decomposes RL environments into structural causal models to unify online, off-policy, and causal learning while defining new tasks including generalized policy learning and counterfactual learning.
A software framework integrates heterogeneous causal inference, policy learning, mediation, forecasts, variance reduction, and anytime-valid inference into one AI-orchestratable interface for business experimentation.
citing papers explorer
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An Introduction to Causal Reinforcement Learning
Proposes causal reinforcement learning (CRL) as a framework that decomposes RL environments into structural causal models to unify online, off-policy, and causal learning while defining new tasks including generalized policy learning and counterfactual learning.
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Closing the Loop: A Software Framework for AI to Support Business Decision Making
A software framework integrates heterogeneous causal inference, policy learning, mediation, forecasts, variance reduction, and anytime-valid inference into one AI-orchestratable interface for business experimentation.