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公共卫生、医学、经济或教育政策、监管政策、商业决策等各个领域的因果效应问题。然而，正是因为其中许多问题都存在很大的利害关系，科学家们经常由于道德或其他限制而无法通过随机对照试验（RCT）（实验研究的黄金标准）来解决这些问题。这往往是在公共利益问题上确定因果关系的最大障碍之一。因果网络框架是一种相对较新的科学方法的阐述，它使人们能够将系统的某些部分对其他部分没有直接影响的假设编成法典（不排除间接影响）。当某些假设成立时，原则上可以使用纯粹的观察数据代替随机对照试验来确定因果效应。然而，现有方法仅在狭窄的假设范围内合理，并且通常不能很好地扩展到大型网络。该项目由研究者、学生、博士后和合作者进行，致力于通过新算法和样本复杂性界限以及大规模中可能出现的相关性强度的界限来扩大此类方法的适用范围。 、稀疏因果网络。从根本上讲，严格的因果推理存在两个障碍：潜在混杂和选择偏差。潜在混杂的发生是因为系统的重要方面无法（或尚未）被观察到。如果仅在与感兴趣的数量相关的特殊情况下记录数据，就会出现选择偏差。全局混杂因素（影响所有可观察量的混杂因素）的存在排除了因果识别的可能性——除非引入额外的假设。其中之一是全局混杂因素范围内的基数；然而，现有方法还需要统计分离假设。该项目的工作旨在放宽这一假设，以支持 Wasserstein 距离中的模型识别。该项目还寻求超越单一的全局混杂因素，转向有效处理多个全局混杂因素。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，这是一个目前具有独特方法的主题。该项目的一个关键目标是提供强信息不等式：一种特殊情况，即强数据处理不等式，已经针对噪声通道的串联（因果网络的最简单示例）进行了研究；但对于具有潜在混杂和选择偏差的网络，这种类型一无所知。该项目的另一个目标是提供因果发现的方法（使用统计数据而不是领域知识来确定网络结构），尽管存在基数限制的全局混杂因素，但该方法仍能有效工作且对噪声具有鲁棒性。该奖项反映了 NSF 的法定使命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，并被认为值得支持。