高维因果推断与变分后门调整
High Dimensional Causal Inference with Variational Backdoor Adjustment
AI总结：
本文提出一种基于生成建模和变分推断的后门调整方法，用于高维治疗和混杂因素下的因果效应估计，并在半合成X射线医学数据等场景中验证了其有效性。
摘要：
后门调整是因果推断中的一种技术，用于仅从观测数据估计干预量。例如，在医疗场景中，后门调整可用于控制混杂因素并估计治疗的有效性。然而，高维治疗和混杂因素带来了一系列潜在陷阱：可处理性、可识别性、优化。在这项工作中，我们采用生成建模方法对高维治疗和混杂因素进行后门调整。我们将后门调整转化为变分推断中的优化问题，不依赖代理变量和隐藏混杂因素。实验上，我们的方法能够在多种高维设置中估计干预似然，包括半合成的X射线医学数据。据我们所知，这是首次在后门调整中所有相关变量均为高维的应用。
英文摘要：
Backdoor adjustment is a technique in causal inference for estimating interventional quantities from purely observational data. For example, in medical settings, backdoor adjustment can be used to control for confounding and estimate the effectiveness of a treatment. However, high dimensional treatments and confounders pose a series of potential pitfalls: tractability, identifiability, optimization. In this work, we take a generative modeling approach to backdoor adjustment for high dimensional treatments and confounders. We cast backdoor adjustment as an optimization problem in variational inference without reliance on proxy variables and hidden confounders. Empirically, our method is able to estimate interventional likelihood in a variety of high dimensional settings, including semi-synthetic X-ray medical data. To the best of our knowledge, this is the first application of backdoor adjustment in which all the relevant variables are high dimensional.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
