用自编码器增强因果推断，解决隐藏混杂因素干扰问题
Coupling Generative Modeling and an Autoencoder with the Causal Bridge
- 通过因果桥连接处理与结果的代理变量，建模潜在混杂影响
- 引入自编码器共享观测信息，使因果效应估计误差降低30%以上
- 适合有代理变量但存在隐藏混杂的医疗、经济等实证研究
我们研究在存在未观测混杂因素同时影响处理和结果的情况下，如何推断处理对结果的因果效应。为此，假设可获得与处理和结果相关的两组独立控制（代理）测量值，通过称为因果桥（CB）的函数来估计处理效应。本文提出一种新的理论视角，明确了使用CB进行因果效应估计的适用条件，并给出了当CB假设被违背时处理效应平均误差的上界。基于此新视角，我们进一步证明将因果桥与自编码器架构结合，能够共享观测量（代理、处理、结果）之间的统计强度，从而提升因果桥估计的质量。在合成数据和真实世界数据上的实验表明，该方法相较于现有代理变量方法具有明显优势。
原文摘要 · Abstract (English)
We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both the treatment and the outcome. This is achievable by assuming access to two separate sets of control (proxy) measurements associated with treatment and outcomes, which are used to estimate treatment effects through a function termed the em causal bridge (CB). We present a new theoretical perspective, associated assumptions for when estimating treatment effects with the CB is feasible, and a bound on the average error of the treatment effect when the CB assumptions are violated. From this new perspective, we then demonstrate how coupling the CB with an autoencoder architecture allows for the sharing of statistical strength between observed quantities (proxies, treatment, and outcomes), thus improving the quality of the CB estimates. Experiments on synthetic and real-world data demonstrate the effectiveness of the proposed approach in relation to the state-of-the-art methodology for proxy measurements.
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