NSF-BSF: AF: Small: Algorithmic and Information-Theoretic Challenges in Causal Inference

NSF-BSF：AF：小：因果推理中的算法和信息论挑战

• 批准号: 2321079
• 负责人: Leonard Schulman
• 金额: $ 61.6万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321079&HistoricalAwards=false
• 关键词: NSF; BSF; AF; Small; Algorithmic

Scientific research is often intended to answer questions of causal effect in various domains such as public health, medicine, economic or educational policy, regulatory policy, business decisions, etc. However, precisely because so much is at stake in many of these questions, scientists are frequently precluded by ethical or other constraints from addressing them with a randomized controlled trial (RCT), the gold standard for experimental research. This has often been one of the greatest barriers to establishing cause and effect in matters of public interest. The framework of causal networks is a relatively recent elaboration of the scientific method which enables one to codify assumptions that some parts of a system have no direct effect on some others (without ruling out indirect effects). When certain assumptions are justified, one can in principle use purely observational data in lieu of RCTs to determine causal effects. However, existing methods are justified only within a narrow range of assumptions and often do not scale well to large networks. This project, to be carried out by the investigator, students, postdocs and collaborators, is dedicated to increasing the range of applicability of such methods with new algorithms and sample complexity bounds, as well as bounds on the strength of correlations that can occur in large, sparse causal networks.At a fundamental level, there are two obstacles to rigorous causal inference: latent confounding and selection bias. Latent confounding occurs because significant aspects of the system cannot (or have not) been observed. Selection bias occurs if data is recorded only under special circumstances that are correlated with the quantities of interest. The presence of a global confounder (one which affects all observables) rules out causal identification---unless additional assumptions are introduced. One such is a cardinality bound on the range of the global confounder; however, existing methods require in addition a statistical separation assumption. Work in this project aims to relax this assumption in favor of model identification in Wasserstein distance. The project also seeks to move beyond a single global confounder to efficient treatment of multiple global confounders. Another goal of the project is to apply causal networks to the analysis of time series data, a topic with a currently distinct methodology. A key goal of the project is to provide strong information inequalities: a special case, strong data processing inequalities, have been studied for concatenations of noisy channels, the simplest example of a causal network; but nothing of this type is known for networks with latent confounding and selection bias. A further goal of the project is to give methods for causal discovery (the use of statistical data rather than domain knowledge to determine network structure) that work efficiently and are robust to noise despite a cardinality-bounded global confounder.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.

科学研究通常旨在回答各种领域中因果关系的问题，例如公共卫生，医学，经济或教育政策，监管政策，业务决策等。但是，这完全是因为在许多问题中，许多问题都受到了危险，科学家经常被道德或其他约束来避免通过随机对照试验（RCT）进行实验的随机控制或其他约束。这通常是在公共利益方面建立因果关系的最大障碍之一。因果网络的框架是对科学方法的相对详细阐述，使人们能够将系统的某些部分对某些部分没有直接影响进行编纂（不排除间接影响）。当某些假设是合理的时，可以原则上使用纯观察数据代替RCT来确定因果效应。但是，现有方法仅在狭窄的假设范围内才是合理的，并且通常不能很好地扩展到大型网络。该项目将由研究人员，学生，博士后和合作者进行，致力于增加具有新算法和样本复杂性界限的这种方法的适用性，以及相关性的强度，这些相关的强度可能会出现在大而稀疏的因果网络中。潜在混杂之所以发生，是因为无法观察到系统的重要方面（或未观察到）。如果仅在与关注量相关的特殊情况下记录数据，则会发生选择偏差。除非引入其他假设，否则全球混杂因素（影响所有可观察到的）的存在排除了因果识别。其中之一是在全球混杂因素范围内绑定的基数。但是，现有方法还需要一个统计分离假设。该项目的工作旨在放宽这一假设，而有利于瓦斯坦斯坦距离中的模型识别。该项目还试图超越单个全球混杂因素，以有效地处理多个全球混杂因素。该项目的另一个目标是将因果网络应用于时间序列数据的分析，这是一个目前具有不同方法的主题。该项目的一个关键目的是提供强大的信息不平等：特殊情况，强大的数据处理不平等现象已被研究以进行嘈杂渠道的串联，这是因果网络的最简单示例；但是，对于具有潜在混杂和选择偏见的网络而言，这种类型的尚无。该项目的另一个目标是提供因果发现（使用统计数据而不是领域知识来确定网络结构的使用方法）的方法，尽管有基于基础性的全球混杂因素，但仍能对噪音有效地工作。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识优点和广泛影响的评估来通过评估来进行评估，以此值得通过评估。