RLMM decouples person-level choice sensitivity from task-level value functions via a parametric RL model with Boltzmann choice and MAP estimation, outperforming tabular MDP-MM in simulations and linking person parameters to performance in real gameplay data.
Darrell and Aitkin, Murray , title =
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A GenAI-based method extracts representations from unstructured data and uses a neural network to fit marginal structural models that recover causal effects of treatment feature sequences including their positions.
citing papers explorer
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Reinforcement Learning Measurement Model
RLMM decouples person-level choice sensitivity from task-level value functions via a parametric RL model with Boltzmann choice and MAP estimation, outperforming tabular MDP-MM in simulations and linking person parameters to performance in real gameplay data.
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GenAI Powered Dynamic Causal Inference with Unstructured Data
A GenAI-based method extracts representations from unstructured data and uses a neural network to fit marginal structural models that recover causal effects of treatment feature sequences including their positions.