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.

理解观察到的现象背后的因果机制是科学的主要目标之一。人们意识到，统计关联本身不足以解释这些机制，这导致研究人员用基于“因果推理”的技术丰富了传统的统计分析。然而，该领域最近的大多数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得不到满足。这个项目试图开发一种合理和普遍的因果推理理论来涵盖这些情况。目标是设计一种智能系统决策的框架，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(规划/推理)执行有效的推理，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)。这一新发现将使经验科学的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。这项研究有望从根本上改变数据科学在违反标准因果假设(即缺失数据、选择偏差和混淆偏差)的领域的实践。决策方面的工作预计将为“自动化科学家”的设计铺平道路，即将观测数据和实验数据结合在一起，进行自己的实验，并决定最佳行动和政策选择的程序。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的原则：(1)参与建立以因果推理为核心的新的“数据科学”课程，以及(2)为学生和公众编写解释因果推理实践的新教材(例如，书)。此外，该项目通过促进一些教育举措，如论坛、讲习班和为编写教育材料创造新的激励措施(如“因果教育奖”)，支持因果推理界。宣称因果关系的存在(结构学习)、因果影响的大小(鉴定)和设计最佳干预措施(决策)是整个数据驱动领域的一些最重要的任务。这个项目研究的是识别、学习和决策环境，其中(1)数据不是随机缺失的，(2)非参数估计是不可行的，(3)聚合行为不能转化为个人层面决策的指导。具体地说，该项目考虑了测量系统扭曲(丢失数据)的问题，这在统计文献中受到了极大的关注，但在数据丢失而不是随机的情况下，基本上没有在因果推断的背景下进行调查。该项目还旨在利用线性模型的特殊性质，这是非参数因果推理最常见的第一近似值，以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，该项目考虑了因果知识和反事实知识如何加快实验速度并支持原则性决策的根本问题。其目标是开发一种完整的算法理论，以确定何时可以从数据中了解特定的因果关系，以及如何将所了解的因果知识(可能通过实验)纳入其中，以便在新的环境条件下摊销。