用核嵌入方法在非参数框架下提升因果推断的准确性
An Overview of Causal Inference using Kernel Embeddings
- 将概率分布映射到再生核希尔伯特空间，实现复杂变量关系的灵活表示
- 可无缝转换观测分布为干预分布，有效估计平均处理效应
- 适合需要处理混杂因素的因果分析研究者
核嵌入已成为多种统计推断问题中表示概率测度的强大工具。通过将概率测度映射到再生核希尔伯特空间（RKHS），核嵌入能够灵活表示变量间的复杂关系，并作为高效传递分布表示至下游任务（如假设检验或因果效应估计）的机制。在因果推断中，主要挑战包括识别因果关联及从存在混杂变量的观察数据中估计平均处理效应。核嵌入提供了一种稳健的非参数框架来应对这些挑战，允许对观测数据分布进行表示，并无缝转换为干预分布表示，从而估计相关因果量。本文综述了近期结合核嵌入表达能力与因果推断的研究进展。
原文摘要 · Abstract (English)
Kernel embeddings have emerged as a powerful tool for representing probability measures in a variety of statistical inference problems. By mapping probability measures into a reproducing kernel Hilbert space (RKHS), kernel embeddings enable flexible representations of complex relationships between variables. They serve as a mechanism for efficiently transferring the representation of a distribution downstream to other tasks, such as hypothesis testing or causal effect estimation. In the context of causal inference, the main challenges include identifying causal associations and estimating the average treatment effect from observational data, where confounding variables may obscure direct cause-and-effect relationships. Kernel embeddings provide a robust nonparametric framework for addressing these challenges. They allow for the representations of distributions of observational data and their seamless transformation into representations of interventional distributions to estimate relevant causal quantities. We overview recent research that leverages the expressiveness of kernel embeddings in tandem with causal inference.
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