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.
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Self-separated and self-connected models for mediator and outcome missingness in mediation analysis
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.