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

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。由于认识到统计关联本身不足以阐明这些机制，研究人员利用基于“因果推断”的技术来丰富传统的统计分析。然而，这一领域的大多数最新进展都是在过于乐观的假设下取得的，而在实际的大规模情况下，这些假设往往无法实现。这个项目旨在发展一个健全的和一般的因果推理理论，以涵盖这些情况。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）使用新的推断表示，基于（1）和（2），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的领域的实践（即，缺失数据、选择偏倚和混杂偏倚）。关于决策的工作预计将为“自动化科学家”的设计铺平道路，即，一个结合观察和实验数据的程序，进行自己的实验，并决定行动和政策的最佳选择。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）参与建立新的“数据科学”课程，其中因果推理发挥核心作用，以及（2）为学生和公众开发新的教育材料，解释因果推理的实践（例如，书）。此外，该项目通过促进一些教育举措，如论坛、讲习班和为教育材料的开发创造新的激励措施（例如，在数据驱动的领域中，最重要的任务是宣称因果关系的存在（结构学习）、因果效应的大小（识别）以及设计最佳干预措施（决策）。该项目研究识别，学习和决策环境，其中（1）数据不是随机缺失的，（2）非参数估计不可行，（3）聚合行为不能转化为个人决策的指导。具体而言，该项目考虑的问题时，测量系统扭曲（缺失数据），这已经收到了大量的关注，在统计文献，但基本上没有在因果推理的背景下，当数据缺失不是随机的。该项目还旨在利用线性模型的特殊性质，最常见的非参数因果推理的第一近似，阐明数据中的因果关系，并促进此类模型的敏感性分析。 最后，该项目考虑了因果和反事实知识如何加速实验和支持原则性决策的基本问题。我们的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，以及如何整合所学习的因果知识（可能通过实验），以便在新的环境条件下摊销。

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.