RI: Medium: Collaborative Research: Causal Inference: Identification, Learning, and Decision-Making

RI：媒介：协作研究：因果推理：识别、学习和决策

• 批准号: 2011463
• 负责人: Elias Bareinboim
• 金额: $ 34.19万
• 依托单位: Columbia University
• 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: 2019-07-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011463&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Causal

Understanding the causal mechanisms underlying an observed phenomenon is one of the primary goals of science. The realization that statistical associations in themselves are insufficient for elucidating those mechanisms has led researchers to enrich traditional statistical analysis with techniques based on "causal inference". Most of the recent advances in the field, however, operate under overly optimistic assumptions, which are often not met in practical, large-scale situations. This project seeks to develop a sound and general causal inference theory to cover those situations. The goal is to design a framework for decision-making of intelligent systems, including (1) learning a causal representation of the data-generating environment (learning), (2) performing efficient inference leveraging the learned model (planning/inference), and (3) using the new inferred representation, based on (1) and (2), to decide how to act next (decision-making). The new finding will benefit investigators in every area of the empirical sciences, including artificial intelligence, machine learning, statistics, economics, and the health and social sciences. The research is expected to fundamentally change the practice of data science in areas where the standard causal assumptions are violated (i.e., missing data, selection bias, and confounding bias). The work on decision-making is expected to pave the way toward the design of an "automated scientist", i.e., a program that combines both observational and experimental data, conducts its own experiments, and decides on the best choices of actions and policies. The project also helps to disseminate the principles of causal inference throughout the sciences by (1) engaging in the establishment of new "data science" curriculum where causal inference plays a central role, and (2) developing new educational materials for students and the general public explaining the practice of causal inference (e.g., book). Furthermore, the project supports the causal inference community by fostering a number of educational initiatives such as forums, workshops, and the creation of new incentives for the development of educational material (e.g., a "Causality Education Award").Making claims about the existence of causal connections (structural learning), the magnitude of causal effects (identification), and designing optimal interventions (decision-making) are some of the most important tasks found throughout data-driven fields. This project studies identification, learning, and decision-making settings where (1) data are missing not at random, (2) non-parametric estimation is not feasible, and (3) aggregated behavior does not translate into guidance for individual-level decision-making. Specifically, the project considers the problem when measurements are systematically distorted (missing data), which has received an enormous amount of attention in the statistical literature, but has not essentially been investigated in the context of causal inference when data are missing not at random. The project further aims to leverage the special properties of linear models, the most common first approximation to non-parametric causal inference, to elucidate causal relationships in data, and to facilitate sensitivity analysis in such models. Finally, the project considers the fundamental problem on how causal and counterfactual knowledge can speed-up experimentation and support principled decision-making. The goal is to develop a complete algorithmic theory to determine when a particular causal effect can be learned from data and how to incorporate causal knowledge learned (possibly by experimentation) so that it can be amortized over new environmental conditions.

了解观察到的现象背后的因果机制是科学的主要目标之一。研究人员认识到统计关联本身不足以阐明这些机制，因此利用基于“因果推理”的技术来丰富传统统计分析。然而，该领域的大多数最新进展都是在过于乐观的假设下进行的，而这些假设在实际的大规模情况下往往无法满足。该项目旨在开发一种合理且通用的因果推理理论来涵盖这些情况。目标是设计一个智能系统决策框架，包括（1）学习数据生成环境的因果表示（学习），（2）利用学习的模型进行有效的推理（规划/推理），以及（3）基于（1）和（2），使用新的推断表示来决定下一步如何行动（决策）。这一新发现将使实证科学各个领域的研究人员受益，包括人工智能、机器学习、统计学、经济学以及健康和社会科学。该研究预计将从根本上改变数据科学在违反标准因果假设（即缺失数据、选择偏差和混杂偏差）领域的实践。决策方面的工作预计将为“自动化科学家”的设计铺平道路，即一个结合观察数据和实验数据、进行自己的实验并决定行动和政策的最佳选择的程序。该项目还通过以下方式帮助在整个科学领域传播因果推理原理：(1) 参与建立新的“数据科学”课程，其中因果推理发挥核心作用；(2) 为学生和公众开发新的教育材料公众解释因果推理的实践（例如书籍）。此外，该项目还通过促进论坛、研讨会等一系列教育举措以及为教育材料的开发制定新的激励措施（例如“因果关系教育奖”）来支持因果推理社区。因果联系（结构学习）、因果效应的大小（识别）和设计最佳干预措施（决策）是整个数据驱动领域中最重要的任务。该项目研究识别、学习和决策设置，其中（1）数据并非随机丢失，（2）非参数估计不可行，（3）聚合行为不能转化为个人层面决策的指导-制作。具体来说，该项目考虑了测量系统性失真（丢失数据）时的问题，该问题在统计文献中受到了大量关注，但在数据非随机丢失时的因果推断背景下基本上没有得到调查。该项目进一步旨在利用线性模型的特殊属性（非参数因果推理最常见的第一近似）来阐明数据中的因果关系，并促进此类模型中的敏感性分析。 最后，该项目考虑了因果和反事实知识如何加速实验并支持原则性决策的基本问题。目标是开发一个完整的算法理论，以确定何时可以从数据中学习特定的因果效应，以及如何整合学到的因果知识（可能通过实验），以便可以在新的环境条件下摊销它。