CAREER: Approximate Causal Inference

职业：近似因果推理

• 批准号: 1750807
• 负责人: Elias Bareinboim
• 金额: $ 49.97万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750807&HistoricalAwards=false
• 关键词: CAREER; Approximate; Causal; Inference

Causality is central to scientific inquiry across the sciences. Without causal information, researchers cannot predict the effects of new interventions, estimate retrospective counterfactuals, and, perhaps more importantly, construct meaningful, in-depth explanations of the phenomenon under investigation. With the unprecedented accumulation of data, the challenge of finding meaningful explanations can be summarized under the rubric of "data-fusion" -- namely, deriving a causal interpretation from a combination of experimental and observational studies collected under disparate, non-exchangeable conditions (Bareinboim and Pearl, Proc. Natl. Acad. Sci. U.S.A, 2016). Despite all the recent progress, it is still non-trivial to apply state-of-the-art causal inference methods in many large-scale settings. In particular, the scientist's available knowledge does not always match what the theory expects, and the theory does not accept as input (and generate as output) more relaxed causal specifications. Given the completeness of the theory, these requirements cannot be strictly waived. In reality, however, some researchers continue to make their claims even when the required conditions are not met. There is an increasing recognition throughout the empirical disciplines that many of the scientific findings articulated today are too fragile, incapable of resisting to a more rigorous scrutiny or even being reproduced. The goal of this project is to bridge the gap between the conditions entailed by the theory (which, if followed, would generate robust and scientifically-grounded claims) and the knowledge available at the hands of the scientist. Specifically, the project seeks (1) to characterize the trade-off between the combination of data and background knowledge (scientific theories) available versus the strength of newly hypothesized causal explanations, and (2) to construct approximation schemes allowing inputs that are coarse and imprecise, while generating outputs that are still causally meaningful. The proposed research is expected to offer foundational grounding for most of the data science inferences made today, which will impact the practice of several data-intensive fields that are built on cause-and-effect relationships, including econometrics, education, bioinformatics, and medicine. The project also contains a significant educational component. Similar to the importance of physics and calculus in basic science education in the 20th century, causal inference will be a vital component of the curriculum of undergraduate studies in a modern, data-rich society. The project develops a new educational platform tailored to teaching causal inference concepts, principles, and tools to STEM students. The primary goal of this new platform is to move from acausal claims obtained from pervasive regression-based techniques, as well as vague and self-evident statements such as "association does not imply causation", and go towards a more fundamental understanding of the conditions necessary to support causal statements. The goal of this proposal is to develop a principled framework for approximations in causal inference. There are two possible approximation dimensions, one regarding the input and the other the output of a given problem instance. First, we will develop sufficient and necessary identification conditions to accept as input a model that is not fully specified (e.g., a causal DAG), but only a coarser description of the phenomenon is available. We will further develop effective procedures for determining whether a causal quantity can be approximated from a combination of observational and experimental datasets, given structural knowledge about the underlying data-generating process. The project will further leverage both results to design efficient learning algorithms under the relaxed assumption that the input is just partially specified and the output can be an approximation of the target causal distribution. Finally, we will consider the problem of learning causal explanations when multiple biased datasets are available, including when plagued with selection bias, confounding bias, and structural heterogeneity. The goal is to develop a general algorithmic theory of approximate causal inference that is capable of producing more robust, reproducible, and generalizable causal explanations.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.

因果关系对于整个科学的科学询问至关重要。没有因果信息，研究人员将无法预测新干预措施的影响，估计回顾性反事实，也许更重要的是，对正在研究的现象构建了有意义的，深入的解释。随着数据的前所未有的积累，可以在“数据融合”的鲁里克总结一下有意义的解释的挑战 - 也就是说，从不同的实验性和观察性研究的结合中得出了因果解释，这些研究和观察性研究在不同的，不可交流的条件下收集了（Bareinboim和Pearl，Pearl，Pearl，Proc.Natl。尽管最近取得了所有进展，但在许多大规模环境中应用最新的因果推理方法仍然是不平凡的。特别是，科学家的可用知识并不总是与理论所期望的相匹配，并且该理论不接受投入（并产生输出）更轻松的因果关系。考虑到理论的完整性，这些要求不能严格放弃。但是，实际上，即使不满足所需条件，一些研究人员仍会继续提出主张。在整个经验学科中，人们的认识越来越多，即当今所阐明的许多科学发现太脆弱了，无法抵抗更严格的审查甚至被复制。该项目的目的是弥合理论所带来的条件之间的差距（如果遵循的话将产生强大而科学的主张），并在科学家手中获得的知识。具体而言，该项目寻求（1）来表征数据和背景知识（科学理论）的组合与新假设的因果解释的强度之间的权衡，并且（2）构建近似方案，允许输入，这些输入是粗糙且不正确的，同时产生仍然有意义的产量。预计拟议的研究将为当今的大多数数据科学推论提供基本基础，这将影响基于因果关系的几个数据密集型领域的实践，包括计量经济学，教育，生物信息学和医学。该项目还包含重要的教育组成部分。与物理学和微积分在20世纪基础科学教育中的重要性相似，因果推断将是现代，数据丰富的社会中本科研究课程的重要组成部分。该项目开发了一个针对教学因果推理概念，原理和工具的新教育平台。这个新平台的主要目的是从基于普遍回归的技术获得的可支配主张，以及含糊和不言而喻的陈述，例如“关联并不意味着因果关系”，并朝着对支持因果关系陈述所必需的条件有了更基本的理解。该提案的目的是开发一个原则上的因果推论近似框架。有两个可能的近似维度，一个关于输入，另一个有关给定问题实例的输出。首先，我们将开发足够和必要的识别条件，以接受未完全指定的模型（例如，因果关系），但只能对该现象进行更粗略的描述。我们将进一步制定有效的程序，以确定是否可以从观察和实验数据集的组合近似因果数量，并给定有关基础数据生成过程的结构知识。该项目将进一步利用这两种结果，以设计有效的学习算法在放松的假设下，即输入只是部分指定，并且输出可以是目标因果分布的近似值。最后，当有多个偏见的数据集可用时，我们将考虑学习因果解释的问题，包括困扰选择偏见，混杂偏见和结构异质性时。目的是开发一种近似因果推断的一般算法理论，该理论能够产生更强大，可再现和可推广的因果解释。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查审查标准来通过评估来通过评估来获得支持的。