MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback
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MOCA：采用单向交叉注意力与反馈切断的模块化因果推断框架
- 研究问题
- 在观察数据的深度学习因果估计中，如何分离处理与结局建模，限制结局信息反馈，并兼顾平均效应估计与个体效应推断？
- 方法与思路
- 作者以单向交叉注意力构建模块化 transformer，用 Markov 核分析处理表示和预测最优性，并提出个体效应的共形推断。
- 材料陈述的贡献
- 摘要称在正确设定下可恢复平均处理效应，并在多组模拟与基准数据中取得有竞争力或更好的估计表现，消融支持模块作用。
- 阅读关注点
- 应核对混杂调整与模型正确设定的条件、预测 KL 最优性和因果误差的联系，以及个体效应共形推断的目标与覆盖条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明完整识别和覆盖条件，架构限制本身不能保证观察数据因果识别。
原文摘要
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 flexibility of transformer architectures. Using a Markov-kernel formulation, we show that MOCA learns an autonomous treatment representation and is predictively KL-optimal within its Gaussian model class. Under correct specification, MOCA recovers the true average treatment effect, while additional two-way feedback does not further reduce average treatment effect estimation error. We also propose a conformal inference procedure for individual treatment effects. Across multiple simulation scenarios, MOCA achieved competitive or improved average treatment effect estimation compared with IPW, AIPW, the X-learner, TARNet, DragonNet, BART, Causal Forest, and Do-PFN. Ablation studies supported the contributions of the proposed architectural components. We further evaluated MOCA on the Infant Health and Development Program benchmark and the observational Dehejia-Wahba dataset. Overall, modular attention with one-way information flow provides an effective and interpretable framework for causal inference using modern deep-learning models.