提出新方法自动学习随时间变化的工具变量，解决时序数据因果推断偏差问题。
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
- 用LSTM与变分自编码器从代理变量中自动学习时变工具变量及其条件集
- 在存在时变潜变量干扰时，显著降低因果效应估计偏差
- 无需领域先验知识，适用于医疗、经济等动态系统因果分析
从时序数据中查询因果效应在医疗、经济、气候科学和流行病学等领域至关重要。然而，当存在随时间变化的潜变量混淆时，这些变量同时影响处理和结果变量，导致因果效应估计产生偏差。传统工具变量（IV）方法受限于预设的工具变量或强假设，在动态场景下难以适用。为此，我们提出一种新型时变条件工具变量（CIV）去偏方法——TDCIV。TDCIV利用长短期记忆网络（LSTM）和变分自编码器（VAE）从代理变量中无监督地解耦并学习时变CIV及其条件集，无需领域先验知识。在马尔可夫性假设和代理变量可用的前提下，理论证明了所学表示的有效性，从而实现准确的因果效应估计。TDCIV是首个不依赖领域知识即可有效学习时变工具变量及其条件集的方法。
原文摘要 · Abstract (English)
Querying causal effects from time-series data is important across various fields, including healthcare, economics, climate science, and epidemiology. However, this task becomes complex in the existence of time-varying latent confounders, which affect both treatment and outcome variables over time and can introduce bias in causal effect estimation. Traditional instrumental variable (IV) methods are limited in addressing such complexities due to the need for predefined IVs or strong assumptions that do not hold in dynamic settings. To tackle these issues, we develop a novel Time-varying Conditional Instrumental Variables (CIV) for Debiasing causal effect estimation, referred to as TDCIV. TDCIV leverages Long Short-Term Memory (LSTM) and Variational Autoencoder (VAE) models to disentangle and learn the representations of time-varying CIV and its conditioning set from proxy variables without prior knowledge. Under the assumptions of the Markov property and availability of proxy variables, we theoretically establish the validity of these learned representations for addressing the biases from time-varying latent confounders, thus enabling accurate causal effect estimation. Our proposed TDCIV is the first to effectively learn time-varying CIV and its associated conditioning set without relying on domain-specific knowledge.
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