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Optimal Counterfactual Explanations in Tree Ensembles

ICML 2021 · Learning theory and algorithms › Combinatorial and search optimization · approach: Predict-then-optimize learning · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use OCEAN instead of feature tweaking (FT)Counterfactual search for tree ensembles · Adult, Credit Card Default, COMPAS, German Credit, Online News, Data Phishing, Spambase, Students Performancel1 distance ratio to optimum: 15.9, 10.2, 4.5, 4.8, 31.7, 1.4; FT found no feasible explanation for SP and STHelped
Add isolation-forest plausibility constraints (ablation)OCEAN · Adult, Credit Card Default, COMPAS, German Credit, Online News, Data Phishing, Spambase, Students Performanceplausible explanations (P): 55% -> 100%; 0% -> 100%; 25% -> 100%; 25% -> 100%; 100% -> 100%; 35% -> 100%; 100% -> 100%; 40% -> 100%Helped
Add isolation-forest plausibility constraints (ablation)OCEAN · Adult, Credit Card Default, COMPAS, German Credit, Online News, Data Phishing, Spambase, Students Performanceexplanation cost (C): 0.21 -> 0.40; 0.03 -> 0.56; 0.12 -> 0.57; 0.09 -> 1.13; 0.01 -> 0.01; 0.78 -> 2.40; 0.02 -> 0.02; 1.18 -> 1.63Mixed
Add isolation-forest plausibility constraints (ablation)OCEAN · Adult, Credit Card Default, COMPAS, German Credit, Online News, Data Phishing, Spambase, Students PerformanceCPU time (s): 0.75 -> 1.82; 0.99 -> 1.54; 0.44 -> 0.85; 0.60 -> 1.71; 1.34 -> 1.64; 0.36 -> 1.81; 2.71 -> 4.43; 0.62 -> 1.36Hurt
Use OCEAN instead of OAECounterfactual search for tree ensembles · Adult, Credit Card Default, German Credit, Online News, Data Phishing, Spambase, Students PerformanceCPU time (s): 28.37 -> 1.22; 5.52 -> 1.34; 5.08 -> 1.16; >900 -> 2.97; 0.94 -> 0.52; >900 -> 2.73; 69.64 -> 1.10Helped
Use OCEAN instead of OAECounterfactual search for tree ensembles · COMPASCPU time (s): 0.38 -> 0.52Hurt

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