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/
Semi-modular inference: Enhanced learning in multi-modular models by tempering the influence of components
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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/