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Table 1 Comparing BCAUS against other deep-learning-based causal inference algorithms

From: Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision

Method In-sample  ϵATE Out-of-sample ϵATE
BNN 0.37 ± .03 0.42 ±.03
BLR 0.72 ±.04 0.93 ±.05
TARNet 0.26 ±.01 0.28 ±.01
CFR MMD 0.30 ±.01 0.31 ±.01
CFR WASS 0.25 ± .01 0.27 ±.01
GANITE 0.43 ± .05 0.49 ± .05
Dragonnet 0.14 ± .01 0.21 ± .01
CEVAE 0.34 ±.01 0.46 ±.02
BART 0.47 ±.02 0.66 ±.03
BCAUS IPTW 0.30 ± .01 0.60 ±.02
BCAUS DR 0.13 ±.00 0.29 ±.01
  1. We include BART for comparison even though it is not neural network based. ϵATE (lower is better) is the mean absolute error between estimated ATE and ground-truth ATE. BNN Balancing Neural Network [21], BLR Balancing Linear Regression [21], TARNet Treatment-Agnostic Representation Network [22], CFR Counterfactual Regression [22], GANITE Generative Adversarial Nets for inference of Individualized Treatment Effects [24], Dragonnet [19], CEVAE Causal Effect Variational Autoencoder [23], BART Bayesian Additive Regression Trees [16]. In-sample value is computed on 672 examples (training + cross-validation) and the out-of-sample value is computed on 75 examples in the hold-out set. The standard error across 1000 realizations is reported as the uncertainty. Performance of BCAUS is comparable to other models