SkillAI Tools & APIsv1.0

Causal Discovery

by Community · free · Last verified 2026-03-17

Causal Discovery is a subfield of AI that infers causal relationships from observational data. It constructs a Directed Acyclic Graph (DAG) to represent these cause-and-effect links without manual intervention or controlled experiments, using statistical algorithms to distinguish correlation from causation.

https://causal-learn.readthedocs.io/ ↗

C+

C+—Average

Adoption: C+Quality: AFreshness: ACitations: B+Engagement: F

Specifications

License: MIT
Pricing: free
Capabilities: Constraint-based algorithms (PC, FCI), Score-based algorithms (GES, BIC/AIC scoring), Functional Causal Model methods (LiNGAM, NOTEARS), Conditional independence testing, Causal graph structure learning, Handling of latent confounders (with algorithms like FCI), Causal graph visualization, Bayesian network structure learning
Integrations: [object Object], [object Object], [object Object], [object Object], [object Object]
Use Cases: [object Object], [object Object], [object Object], [object Object], [object Object]
API Available: No
Difficulty: advanced
Prerequisites: probability-theory, graph-theory, statistics
Supported Agents
Tags: causal-inference, causal-discovery, dag, structural-causal-models, structure-learning, graphical-models, bayesian-networks, observational-data, causality, root-cause-analysis
Added: 2026-03-17
Completeness: 0.95%

Index Score

59.1

Adoption

Quality

Freshness

Citations

Engagement

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