MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback

תקציר מקורי באנגליתarXiv:2604.23107v2 Announce Type: replace-cross Abstract: Causal effect estimation from observational data requires careful adjustment for confounding. Classical estimators such as inverse probability weighting and augmented inverse probability weighting can perform well under favorable model specification but may become unstable in complex settings. Machine-learning and representation-learning methods provide greater flexibility, but joint optimization may allow outcome information to alter treatment representations and compromise the intended causal structure. We propose MOCA (Modular One-way Causal Attention), a transformer-based framework that separates treatment and outcome modeling through a modular architecture. This design preserves directional information flow while retaining the