causal-discovery

This paper evaluates the practical effectiveness of Markov boundaries for tabular prediction, finding that while theoretically optimal, current causal discovery methods fail to consistently improve predictive performance due to computational limitations and mismatched optimization goals.

CausaLab is a scalable environment for evaluating LLM agents on interactive causal discovery, assessing both predictive accuracy and faithful recovery of underlying causal mechanisms. Experiments reveal a gap between prediction and mechanism recovery, highlighting limits in current LLM agents as experimental causal reasoners.

This paper argues that scalar edge scores in nonlinear causal discovery obscure state-dependent effects, and proposes function-valued causal influence using Neural Additive Vector Autoregression and Individual Conditional Expectation.

This paper introduces score-based methods for causal discovery in the presence of latent variables, offering theoretical guarantees of consistency and score equivalence, and unifies several constraint-based approaches.

PACER is a new scalable framework for causal discovery from large-scale interventional data that guarantees acyclicity by design, achieving up to two orders of magnitude speedups over penalty-based methods on benchmarks with thousands of variables.

The paper presents Prometheus, a framework that uses large language models to extract local causal claims from text and organizes them into navigable causal atlases, enabling deep causal research across diverse domains.

The paper introduces TTCD, a novel framework for temporal causal discovery from non-stationary time series data using transformer-based feature learning and reconstruction-guided signal distillation.