MOCA is a new modular transformer architecture for causal inference that applies one-way cross-attention and gradient detachment to keep treatment and outcome modeling separate, showing competitive or better performance than IPW, AIPW, X-learner, TARNet, and DragonNet on simulations and real health/
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Incorporating unlabeled auxiliary covariates lowers the efficiency bound for treatment effect estimation and produces estimators with smaller asymptotic variance than those without the auxiliary data.
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MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback
MOCA is a new modular transformer architecture for causal inference that applies one-way cross-attention and gradient detachment to keep treatment and outcome modeling separate, showing competitive or better performance than IPW, AIPW, X-learner, TARNet, and DragonNet on simulations and real health/
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Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
Incorporating unlabeled auxiliary covariates lowers the efficiency bound for treatment effect estimation and produces estimators with smaller asymptotic variance than those without the auxiliary data.