CAREER: Approximate Causal Inference

职业：近似因果推理

基本信息

批准号：
2011497
负责人：
Elias Bareinboim
金额：
$ 39.15万
依托单位：
Columbia University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2023-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011497&HistoricalAwards=false
关键词：
CAREER Approximate Causal Inference

项目摘要

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.

因果关系是跨学科科学探究的核心。如果没有因果信息，研究人员就无法预测新干预措施的效果，无法估计回顾性反事实，或许更重要的是，无法对所调查的现象构建有意义、深入的解释。随着数据空前的积累，寻找有意义的解释的挑战可以概括为“数据融合”——即，从不同的、不可交换的条件下收集的实验和观察研究的结合中得出因果解释。巴林博伊姆和珀尔，美国科学院院刊，2016 年。尽管最近取得了所有进展，但在许多大规模环境中应用最先进的因果推理方法仍然很重要。特别是，科学家的现有知识并不总是符合理论的预期，并且理论不接受更宽松的因果规范作为输入（并生成作为输出）。鉴于理论的完整性，不能严格放弃这些要求。然而实际上，即使不满足所需条件，一些研究人员仍继续提出自己的主张。整个实证学科越来越认识到，今天阐述的许多科学发现都过于脆弱，无法经受更严格的审查，甚至无法被复制。该项目的目标是弥合理论所要求的条件（如果遵循该理论，将产生强有力且有科学依据的主张）与科学家掌握的知识之间的差距。具体来说，该项目寻求（1）描述可用数据和背景知识（科学理论）的组合与新假设的因果解释的强度之间的权衡，以及（2）构建近似方案，允许粗略的输入和不精确，同时产生仍然具有因果意义的输出。拟议的研究预计将为当今大多数数据科学推论提供基础，这将影响建立在因果关系基础上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内容。类似于物理和微积分在 20 世纪基础科学教育中的重要性，因果推理将成为现代数据丰富的社会中本科学习课程的重要组成部分。该项目开发了一个新的教育平台，专门为 STEM 学生教授因果推理概念、原理和工具。这个新平台的主要目标是摆脱从普遍的基于回归的技术中获得的非因果主张，以及诸如“关联并不意味着因果关系”等模糊且不言而喻的陈述，并转向对条件的更基本理解支持因果陈述所必需的。该提案的目标是为因果推理中的近似开发一个原则框架。有两种可能的近似维度，一种与给定问题实例的输入有关，另一种与给定问题实例的输出有关。首先，我们将制定足够且必要的识别条件来接受未完全指定的模型（例如因果 DAG）作为输入，但只能对现象进行更粗略的描述。考虑到有关底层数据生成过程的结构知识，我们将进一步开发有效的程序，以确定是否可以通过观察和实验数据集的组合来近似因果量。该项目将进一步利用这两个结果，在输入只是部分指定且输出可以是目标因果分布的近似值的宽松假设下设计高效的学习算法。最后，我们将考虑当存在多个有偏差的数据集时，包括当受到选择偏差、混杂偏差和结构异质性困扰时，学习因果解释的问题。目标是开发一种近似因果推理的通用算法理论，能够产生更稳健、可重复和可概括的因果解释。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。