解决长期治疗效应估计中的单调缺失问题，提升结果可靠性。
Identification and Estimation of Long-Term Treatment Effects with Monotone Missing
- 提出顺序缺失假设与三种新估计方法
- 在两个基准数据集上验证方法有效性
- 引入BalanceNet降低数据稀疏导致的方差
长期治疗效应估计在多个领域有广泛应用。其关键特征是长期结果收集通常为多阶段过程，且存在单调缺失——即早期缺失者后续阶段仍缺失。尽管该现象普遍，但以往研究对此关注极少。本文通过引入顺序缺失假设填补该空白，提出三种新估计方法：逆概率加权、顺序回归插补和顺序边际结构模型（SeqMSM）。考虑到SeqMSM因单调缺失导致的数据稀疏而可能产生高方差，进一步提出一种增强平衡的新型方法BalanceNet，以提高估计的稳定性和准确性。在两个广泛使用的基准数据集上的大量实验表明，所提方法有效。
原文摘要 · Abstract (English)
Estimating long-term treatment effects has a wide range of applications in various domains. A key feature in this context is that collecting long-term outcomes typically involves a multi-stage process and is subject to monotone missing, where individuals missing at an earlier stage remain missing at subsequent stages. Despite its prevalence, monotone missing has been rarely explored in previous studies on estimating long-term treatment effects. In this paper, we address this gap by introducing the sequential missingness assumption for identification. We propose three novel estimation methods, including inverse probability weighting, sequential regression imputation, and sequential marginal structural model (SeqMSM). Considering that the SeqMSM method may suffer from high variance due to severe data sparsity caused by monotone missing, we further propose a novel balancing-enhanced approach, BalanceNet, to improve the stability and accuracy of the estimation methods. Extensive experiments on two widely used benchmark datasets demonstrate the effectiveness of our proposed methods.
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