Collaborative Research: EAGER: RI: Causal Decision-Making

人工智能（AI）在社会中扮演着越来越重要的角色，因为曾经由人类做出的决策现在被委托给自动化系统。这些系统应该是高效、稳健、可解释、可推广的，并导致社会商定的结果。人们越来越认识到，稳健的决策依赖于对环境背后的因果机制的一些了解。例如，智能机器人必须知道其环境中的因果关系，以更稳健地规划其行动方案;医生需要了解可用药物的效果，以便为患者设计有效的策略。目前这一代负责决策的人工智能系统并没有明确地表示潜在的因果模型。该项目将为一个总体框架奠定基础，即，一套原则，算法和工具-通过丰富传统的人工智能形式主义与因果成分，为决策系统提供更有效，更强大，更可解释的决策。这项研究将为决策领域的转型播下种子，并对开发下一代人工智能系统产生影响。研究结果预计将对人工智能基础产生重大影响，并可能对社会产生广泛影响，因为越来越多的决策被委托给人工智能系统。研究人员将在因果推理方面开发新的教育材料和课程。研究人员将为研究生提供研究培训，并致力于继续从代表性不足的群体中招募。研究团队将继续支持“统计因果教育奖”，以改善统计和数据科学中现代因果推理工具的教学和学习。该项目是将因果推理（CI）和强化学习（RL）整合到因果强化学习（CRL）学科中的第一步。这个想法是赋予RL代理一个明确的环境因果模型和新的干预和反事实推理能力。CRL将开辟一个新的学习机会和挑战的家庭，以前既不承认也不理解。 这项研究的任务包括当代理人具有不同的感知和驱动能力时，整合离线和在线方法，并开发用于反事实决策的通用机制，这比其标准的干预性对应物更强大。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Artificial intelligence (AI) plays an increasingly prominent role in society since decisions that were once made by humans are now being delegated to automated systems. These systems are expected to be efficient, robust, explainable, generalizable, and lead to outcomes agreed upon by society. There is a growing understanding that robust decision-making relies on some knowledge of the causal mechanisms underlying the environment. For instance, an intelligent robot has to know the cause and effect relationships in its environment to plan its course of action more robustly; a physician needs to understand the effects of available drugs to design an effective strategy for her patients. The current generation of AI systems responsible for decision-making does not explicitly represent the underlying causal model. This project will build the foundations toward a general framework — i.e., a set of principles, algorithms, and tools — for decision-making systems by enriching the traditional AI formalism with causal ingredients for more efficient, robust, and explainable decision-making. The research will plant the seed for a transformation in the decision-making field and have consequences for developing the next generation of AI systems. The research results are expected to have significant impacts on AI foundations and may potentially have broad implications for society as more and more decisions are being delegated to AI systems. The researchers will develop new educational materials and course curricula in causal inference. The researchers will provide research training for graduate students and are committed to continuing to recruit from underrepresented groups. The research team will continue supporting the “Causality in Statistics Education Award” to improve the teaching and learning of modern causal inference tools in statistics and the data sciences.This project is the first step toward the integration of causal inference (CI) and reinforcement learning (RL) into the discipline of causal reinforcement learning (CRL). The idea is to endow an RL agent with an explicit causal model of the environment and new capabilities for interventional and counterfactual reasoning. CRL will open a new family of learning opportunities and challenges that were neither acknowledged nor understood before. The tasks included in this research include integrating offline and online methods when the agents have different perceptual and actuation capabilities and developing general machinery for counterfactual decision-making, which is more powerful than its standard, interventional counterpart.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.