New Methods in High-Dimensional Causal Inference

随着深度学习算法在科学文献中的普及，以及谷歌、微软和Facebook等公司对它们的使用和开发，它们在各种任务中的部署正在迅速加速。 正因为如此，需要对深度学习有更好的数学理解，更广泛地说，需要决策过程中的机器学习算法。 需要的一个关键创新是如何将机器学习算法与因果建模程序结合起来，这将有助于算法开发“推理能力”（例如，理解算法为什么做出它所做出的决定/预测）。 本计画所要解决的问题是如何建立混杂因素的模型，以发展具有理想抽样性质的因果效应估计量。 此外，重要的是要有合理的程序，计算规模与观察的数量。 在这个项目中，PI和团队将把研究重点放在两个领域。 首先是了解深度学习算法的含义及其对流行的潜在结果模型的基本假设的性能。 在最近的工作中，PI发现了高斯过程分类算法，协变量重叠和因果效应估计的规律性之间的基本张力。 这项研究的第一个目标是看看类似的现象是否适用于深度学习算法。 此外，还将探索高斯过程和基于深度学习的分类算法的插值特性。 该项目的第二部分将涉及开发因果效应估计的可扩展算法。 作为这项研究的一部分，将开发用于因果效应估计的新的计算可扩展算法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the popularity of deep learning algorithms in the scientific literature as well as by their use and development by companies such as Google, Microsoft and Facebook, their deployment in a variety of tasks is rapidly accelerating. Because of this, better mathematical understanding of deep learning, and more broadly, machine learning algorithms in decision making processes are needed. A key innovation that is needed is how to marry machine learning algorithms into causal modelling procedures, which will help algorithms to develop "reasoning capabilities" (e.g., understand why an algorithm is making the decision/prediction that it makes). The problem that will be addressed in this project is how to model confounders so as to develop causal effect estimators that have desirable sampling properties. In addition, it is important to have well-justified procedures that computationally scale with the number of observations. In this project, the PI and team will focus their research in two areas. The first will be to understand the implications of deep learning algorithms and their performance on foundational assumptions for the popular potential outcomes model. In recent work, the PI discovered a fundamental tension between Gaussian process classification algorithms, covariate overlap and regularity of causal effect estimators. The goal of the first aim of the research will be to see if a similar phenomenon holds for deep learning algorithms. In addition, the interpolation properties of Gaussian process and deep learning-based classification algorithms will be explored. The second part of the project will deal with developing scalable algorithms for causal effect estimation. New computationally scalable algorithms for causal effect estimation will be developed as part of this research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.