标题： 高维因果推断的平衡方法 显示英文标题

标题： Balancing Method for High Dimensional Causal Inference

摘要： 因果推断在不同领域受到了广泛关注，从经济学、统计学、教育学、医学到机器学习。 在这个领域中，在观察性研究中推断个体层面的因果效应已成为一个重要任务，尤其是在高维设置中。 在本文中，我们提出了一种框架，用于在高维非实验数据中估计个体化治疗效应。 我们提供了理论和实证依据，后者通过将我们的框架与当前表现最好的方法进行比较来证明。 我们的框架在大多数情况下可以与最先进的方法相媲美，而且在简单性和易于实现方面具有优势，同时在某些情况下甚至表现更好。 显示英文摘要

摘要： Causal inference has received great attention across different fields from economics, statistics, education, medicine, to machine learning. Within this area, inferring causal effects at individual level in observational studies has become an important task, especially in high dimensional settings. In this paper, we propose a framework for estimating Individualized Treatment Effects in high-dimensional non-experimental data. We provide both theoretical and empirical justifications, the latter by comparing our framework with current best-performing methods. Our proposed framework rivals the state-of-the-art methods in most settings and even outperforms them while being much simpler and easier to implement.