FoundCause is a transformer-based amortized model for causal graph discovery that explicitly models latent confounders via learnable tokens and reports better performance than prior methods on 15 real-world datasets.
Dowhy: An end-to-end library for causal inference
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative 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.
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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FoundCause: Causal Discovery with Latent Confounders from Observational Data
FoundCause is a transformer-based amortized model for causal graph discovery that explicitly models latent confounders via learnable tokens and reports better performance than prior methods on 15 real-world datasets.
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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.