Non-parametric closed-form bounds on counterfactual MDP transitions across compatible causal models, supporting robust policy optimization under interval uncertainty.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
Introduces CounterBench benchmark and CoIn iterative reasoning method showing LLMs perform near random on formal counterfactual tasks but improve substantially with guided backtracking.
citing papers explorer
-
Robust Counterfactual Inference in Markov Decision Processes
Non-parametric closed-form bounds on counterfactual MDP transitions across compatible causal models, supporting robust policy optimization under interval uncertainty.
-
CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models
Introduces CounterBench benchmark and CoIn iterative reasoning method showing LLMs perform near random on formal counterfactual tasks but improve substantially with guided backtracking.