Causal Inference On Ihdp

Métriques

Average Treatment Effect Error

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
Random Forest	0.96	Estimating individual treatment effect: generalization bounds and algorithms
Balancing Linear Regression	0.93	Estimating individual treatment effect: generalization bounds and algorithms
k-NN	0.79	Estimating individual treatment effect: generalization bounds and algorithms
CEVAE	0.46	Causal Effect Inference with Deep Latent-Variable Models
Balancing Neural Network	0.42	Estimating individual treatment effect: generalization bounds and algorithms
Causal Forest	0.4	Estimating individual treatment effect: generalization bounds and algorithms
BCAUS DR	0.29	Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision
TARNet	0.28	Estimating individual treatment effect: generalization bounds and algorithms
Counterfactual Regression + WASS	0.27	Estimating individual treatment effect: generalization bounds and algorithms
MTDL-KNN	0.23	Deep representation learning for individualized treatment effect estimation using electronic health records
Dragonnet	0.20	Adapting Neural Networks for the Estimation of Treatment Effects
BCAUSS	0.15	Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation

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