Research map / Probabilistic and scientific modeling / Causal discovery
Sensitivity Analysis of Linear Structural Causal Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add bidirected-edge constraints via PushForward to QID (CID vs QID) (ablation) | CID · 3-node, both edge types | queries solved: 68 (21%) -> 320 (100%) | Helped |
| Add PushForward to QID (ablation) | CID · 4-node, both edge types | queries solved: 170,304(29%) -> 555,758(96%) | Helped |
| Add PushForward to QID (ablation) | CID · 4-node, bidirected only | queries solved: – (0%) -> 50,708(97%) | Helped |
| Add PushForward to QID (ablation) | CID · 3-node, bidirected only | queries solved: – (0%) -> 109(100%) | Helped |
| Add PushForward to QID, directed-only parameters (ablation) | CID · 4-node, directed | queries solved: 14,952(95%) -> 14,952(95%) | No clear effect |
| Topological ordering vs brute-force subset search for pushing bidirected edges (ablation) | CID · 3 and 4-node models | queries solved | No clear effect |
| Replace incomplete QID with complete identification algorithm for directed edges (ablation) | CID · 4-node models | queries solved: over 99.99% identified | Helped |
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- Causal learning with the invariance principle (NeurIPS 2026 · similarity 0.88)
- On the Complexity of Identification in Linear Structural Causal Models (NeurIPS 2024 · similarity 0.88)
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