Research map / Probabilistic and scientific modeling / Causal discovery
Conditions and Assumptions for Constraint-based Causal Structure Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Assume faithfulness between P and G_0 (vs. weaker assumptions) | constraint-based natural structure-learning algorithms · theoretical (no experiments) | Markov equivalence of output to G_0 | Helped |
| Replace faithfulness with converse pairwise Markov property plus ordered upward/downward stability (Theorem conditions) | natural structure-learning algorithms · theoretical (no experiments) | skeleton equality sk(G_0)=sk(G(P)) | No clear effect |
| Add path-stability (or V-stability for DAG-generating algorithms) on top of ordered stability and converse pairwise Markov property | natural structure-learning algorithms · theoretical (no experiments) | Markov equivalence of output to G_0 | Helped |
Closest papers by meaning
- Constraint-based Causal Structure Learning with Consistent Separating Sets (NeurIPS 2019 · similarity 0.87)
- Differentiable Structure Learning with Ancestral Constraints (ICML 2025 · similarity 0.86)
- A Fixed-Point Approach for Causal Generative Modeling (ICML 2024 · similarity 0.86)
- Causal-learn: Causal Discovery in Python (NeurIPS 2024 · similarity 0.85)
- Learning Structural Causal Models from Ordering: Identifiable Flow Models (AAAI 2025 · similarity 0.85)
- CausalTab: Pretraining Across Causal Environments for Tabular Causal Discovery (NeurIPS 2026 · similarity 0.84)
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