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On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs

ICML 2023 · Probabilistic and scientific modeling › Treatment effects · approach: Meta-learners and partial identification · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use ISL instead of NOTEARSCausal graph estimation · S2, n=500SHD: 3.06(3.14) -> 0.25(0.86)Helped
Use ISL instead of NOTEARSCausal graph estimation · S3, n=500SHD: 2.52(1.97) -> 0.04(0.24)Helped
Increase sample size from n=500 to n=1000ISL · S3SHD: 0.04(0.24) -> 0.00(0.00)Helped
Use higher-dimensional setting S4 with more potential mediatorsISL · S4, n=1000TPR: 0.90(0.02) TPR; 14.67(3.45) SHDMixed
Use higher-dimensional setting S5 with more potential moderatorsISL · S5, n=1000SHD: 0.43(2.53)Helped

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