Balanced Twins: Causal Inference on Time Series with Hidden Confounding
平衡孪生：存在隐藏混杂的时间序列因果推断AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要围绕隐藏混杂、错时干预、个体反事实及已处理者平均效应展开，直接对应因果推断。
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平衡孪生：存在隐藏混杂的时间序列因果推断
- 研究问题
- 干预错时发生且个体历史异质、处理分配受隐藏因素影响时，如何先恢复个体反事实，再估计已处理者平均效应？
- 方法与思路
- 作者用神经网络联合学习个体序列的低维潜在表示和倾向得分，再通过不受经典凸性约束的灵活匹配估计个体效应。
- 材料陈述的贡献
- 摘要称，该框架能容纳错时干预并改善潜在偏误下的反事实估计，展示了能源消费和临床时间序列中的应用。
- 阅读关注点
- 应核对潜在表示足以处理隐藏混杂的依据、匹配与重叠要求，以及个体效应聚合为 ATT 时的目标定义和验证方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明反事实识别条件，学习潜在表示并不自动解决隐藏混杂。
原文摘要
Accurately estimating treatment effects in time series is essential for evaluating interventions in real-world applications, especially when treatment assignment is biased by unobserved factors. In many practical settings, interventions are adopted at different times across individuals, leading to staggered treatment exposure and heterogeneous pre-treatment histories. In such cases, aggregating outcome trajectories across treated units is ill-defined, making individual treatment effect (ITE) estimation a prerequisite for reliable causal inference. We therefore study the problem of estimating the average treatment effect for the treated (ATT) by first recovering individual-level counterfactuals. We introduce a neural framework that learns simultaneously low-dimensional latent representations of individual time series and propensity scores. These estimates are then used to approximate the individual treatment effects through a flexible matching procedure that avoids classical convexity constraints commonly used in synthetic control methods. By operating at the individual level, our approach naturally accommodates staggered interventions and improves counterfactual estimation under latent bias, without relying on explicit temporal modeling assumptions. We illustrate our approach on both real-world energy consumption data and clinical time series, including high-frequency electricity demand-response programs and semi-synthetic data for individuals in intensive care unit (ICU), where hidden confounding, staggered treatment adoption, and non-stationary dynamics are prevalent.