Introduces CounterBench benchmark and CoIn iterative reasoning method showing LLMs perform near random on formal counterfactual tasks but improve substantially with guided backtracking.
Dowhy: An end-to-end library for causal inference
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A framework using structural causal models simulates parametric drifts to evaluate classifier robustness more realistically than static tests or noise perturbations.
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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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.
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Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
A framework using structural causal models simulates parametric drifts to evaluate classifier robustness more realistically than static tests or noise perturbations.
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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.