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Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise

ICLR 2023 · Probabilistic and scientific modeling › Causal discovery · approach: Interventional causal discovery · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add instantaneous effects to Rhino (ablation)Rhino on Netsim Sim-3.mat · Netsim Sim-3.matAUROC: 0.93 ± 0.006 -> 0.99 ± 0.001Helped
Add history-dependent noise instead of independent Gaussian noise (ablation)Rhino on Netsim Sim-3.mat · Netsim Sim-3.matAUROC: 0.974 ± 0.002 -> 0.99 ± 0.001Helped
Add history-dependent noise instead of independent Gaussian noise (ablation)Rhino on DREAM3 · DREAM3AUROC: 0.673 ± 0.013 -> 0.685 ± 0.003; 0.665 ± 0.009 -> 0.680 ± 0.007; 0.659 ± 0.005 -> 0.664 ± 0.006; 0.598 ± 0.004 -> 0.585 ±Mixed
Set maximum lag to 1 instead of 2 (ablation)Rhino · Synthetic, dimensions 5, 10, 20, and 40F1 Temporal: 0.34 ± 0.12 -> 0.59 ± 0.20; 0.28 ± 0.11 -> 0.49 ± 0.18; 0.25 ± 0.13 -> 0.39 ± 0.22; 0.20 ± 0.18 -> 0.37 ± 0.30Hurt
Set maximum lag to 3 instead of 2 (ablation)Rhino · Synthetic, dimensions 5, 10, 20, and 40F1 Temporal: 0.59 ± 0.20 -> 0.56 ± 0.22; 0.49 ± 0.18 -> 0.45 ± 0.20; 0.39 ± 0.22 -> 0.37 ± 0.21; 0.37 ± 0.30 -> 0.32 ± 0.29No clear effect

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