causal-discovery

NEO is a new type of world model that learns to discover reusable building blocks of explanation from raw observations without supervision or language, selected as an ICML 2026 oral presentation.

Proposes GRACE, a method that combines constraint-based skeleton with gated refinement using L0 regularization for efficient and accurate causal edge discovery in high-dimensional time series. It outperforms existing methods in F1 and speed, demonstrated on synthetic and real-world river flow data.

This survey provides a systematic review of federated causal discovery and inference, organizing methods by methodological paradigm, federation topology, and structural scope, and highlighting open challenges.

This paper argues that language model agents should assist causal discovery workflows by providing contextual support and explanations rather than generating causal conclusions, and introduces causal-learn+ platform to demonstrate this principle.

This paper proposes an interpretable causal-discovery-guided framework for deriving a Sleep Recovery Score (SRS) from multimodal polysomnography data, demonstrating up to 2.5× stronger alignment with perceived recovery than the traditional Apnea–Hypopnea Index (AHI), with potential applications in connected health.

FoundCause is an amortized causal discovery model that explicitly handles latent confounders and missing data, outperforming 15 existing methods on real-world datasets with a single forward pass.

This paper proposes LMT, a Bayesian causal discovery framework that combines LLM-extracted semantic signals from textual alarm records with timestamp-based statistical evidence to infer causal graphs in manufacturing systems.

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.