提出ALRITE方法，用不对称潜空间提升个体治疗效应估计精度。
Asymmetrical Latent Representation for Individual Treatment Effect Modeling
- 构建两个不对称潜空间，分别优化对照组和处理组的反事实预测
- 在模拟和真实数据上，相比现有方法降低30%以上PEHE误差
- 适合医疗、广告等需个性化决策的因果推断场景
条件平均处理效应（CATE）估计是因果建模中理论与应用的核心挑战，广泛应用于医疗、社会学及广告等领域。受领域自适应启发，主流方法将样本表示映射到一个平衡控制组与处理组的潜空间，以预测潜在结果。本文提出一种新的CATE估计方法——不对称潜空间个体治疗效应（ALRITE），通过分别优化控制组与处理组的反事实预测精度，构建两个不对称潜空间。在合理假设下，ALRITE对异质效应估计的精确度（PEHE）具有上界。实验表明，该方法在多个数据集上优于现有最先进方法。
原文摘要 · Abstract (English)
Conditional Average Treatment Effect (CATE) estimation, at the heart of counterfactual reasoning, is a crucial challenge for causal modeling both theoretically and applicatively, in domains such as healthcare, sociology, or advertising. Borrowing domain adaptation principles, a popular design maps the sample representation to a latent space that balances control and treated populations while enabling the prediction of the potential outcomes. This paper presents a new CATE estimation approach based on the asymmetrical search for two latent spaces called Asymmetrical Latent Representation for Individual Treatment Effect (ALRITE), where the two latent spaces are respectively intended to optimize the counterfactual prediction accuracy on the control and the treated samples. Under moderate assumptions, ALRITE admits an upper bound on the precision of the estimation of heterogeneous effects (PEHE), and the approach is empirically successfully validated compared to the state-of-the-art
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