New Methods in High-Dimensional Causal Inference

高维因果推理的新方法

• 批准号: 1914937
• 负责人: Debashis Ghosh
• 金额: $ 14.98万
• 依托单位: University of Colorado at Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914937&HistoricalAwards=false
• 关键词: New; Methods; Dimensional; Causal; Inference

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.

随着深度学习算法在科学文献中的流行以及谷歌、微软和 Facebook 等公司的使用和开发，它们在各种任务中的部署正在迅速加速。 因此，需要对深度学习以及更广泛的决策过程中的机器学习算法有更好的数学理解。 所需的一项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，理解算法为何做出决策/预测）。 该项目将解决的问题是如何对混杂因素进行建模，以便开发具有理想采样特性的因果效应估计器。 此外，重要的是要有合理的程序，可以根据观察的数量进行计算缩放。 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器的规律性之间的根本矛盾。 该研究的第一个目标是看看深度学习算法是否存在类似的现象。 此外，还将探讨高斯过程的插值特性和基于深度学习的分类算法。 该项目的第二部分将开发用于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智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Analysis of regression discontinuity designs with censored data

使用删失数据分析回归不连续性设计

• 发表时间: 2021
• 期刊: Journal of statistical research University of Dacca Institute of Statistical Research and Training
• 作者: Cho, Youngjoo;Hu, Chen;Ghosh, Debashis
• 通讯作者: Ghosh, Debashis

Nonlinear predictive directions in clinical trials

临床试验中的非线性预测方向

• DOI: 10.1016/j.csda.2022.107476
• 发表时间: 2022
• 期刊: Computational Statistics & Data Analysis
• 影响因子: 1.8
• 作者: Cho, Youngjoo;Zhan, Xiang;Ghosh, Debashis
• 通讯作者: Ghosh, Debashis

A Gaussian Process Framework for Overlap and Causal Effect Estimation with High-Dimensional Covariates

用于高维协变量重叠和因果效应估计的高斯过程框架

• DOI: 10.1515/jci-2018-0024
• 发表时间: 2019
• 期刊: Journal of causal inference
• 影响因子: 1.4
• 作者: Ghosh, Debashis;Cruz-Cortés, Efrén
• 通讯作者: Cruz-Cortés, Efrén

High-dimensional causal mediation analysis based on partial linear structural equation models

• DOI: 10.1016/j.csda.2022.107501
• 发表时间: 2022-04
• 期刊: Comput. Stat. Data Anal.
• 作者: Xizhen Cai;Yeying Zhu;Yuan Huang;Debashis Ghosh