Research map / Probabilistic and scientific modeling / Causal discovery
Permutation-based Causal Inference Algorithms with Interventions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use IGSP instead of GIES | causal DAG learning · random Gaussian DAG simulations | proportion of consistently estimated DAGs | Mixed |
| use IGSP instead of GIES | causal DAG learning · GIES-inconsistency simulation | proportion of consistently estimated DAGs | Helped |
| use IGSP instead of GIES | causal DAG learning · Sachs protein signaling data | ROC accuracy | Helped |
| use IGSP instead of GIES | causal DAG learning · perturb-seq gene expression data | held-out intervention prediction | Helped |
| remove the slack parameter (ablation) | interventional Algorithm 1 · interventional consistency counterexample | consistency: probability 1/2 | Hurt |
| use GIES rather than a consistent permutation-based search | causal DAG learning · constructed interventional counterexample | consistency: probability 1/2 | Hurt |
| use interventional rather than observational-only data | IGSP · causal DAG learning | number of search queries | Helped |
| use kernel-based rather than linear Gaussian CI tests | IGSP · Sachs protein signaling data | ROC accuracy | No clear effect |
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