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CAREER: Foundations for Bayesian Nonparametric Causal Inference

职业：贝叶斯非参数因果推理基础

基本信息

批准号：
2144933
负责人：
Antonio Linero
金额：
$ 40万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2027-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144933&HistoricalAwards=false
关键词：
CAREER Foundations Bayesian Nonparametric Causal

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Fueled by the remarkable recent success of machine learning and artificial intelligence, there has been substantial interest in recent years in using machine learning to shed light on important policy questions. Examples of such questions include "will this treatment for a disease improve the quality of life of a particular patient?" and "will participation in this academic program increase student achievement?" Applying state-of-the-art predictive algorithms to inform policy has received tremendous attention over the past decade, with contributions made by econometricians, statisticians, and computer scientists. Despite the successes of machine learning, there exist pitfalls which are not well-understood by practitioners. We argue that the apparent flexibility of machine learning leads indirectly to a sort of rigidity, with the consequence that the results of an analysis may be a foregone conclusion, driven only by the choice to use a flexible model rather than any empirical data. For example, it is a common popular refrain that "correlation is not causation" and that one must be wary of common-causes which can explain an apparent causal relation; we show, however, that poorly designed machine learning methods behave much as humans do and are biased in some sense towards attributing correlations as causal relations. The overall objective of this proposal is to understand and correct for the hidden assumptions underlying a particular type of algorithms based on Bayesian inference, to develop robust methodology based on these insights, and to begin the development of an overarching computational framework for implementing policy-oriented Bayesian machine learning methods in practice.The appeal of Bayesian machine learning is that it promises to marry the predictive accuracy of modern machine learning and the principled uncertainty quantification of Bayesian inference. The indirect nature of prior specification for many problems leads, however, to a phenomenon we refer to as prior dogmatism: due to the inherent properties of independent priors on high-dimensional spaces, poorly designed Bayesian models can exhibit extreme bias towards the hypothesis that the amount of confounding is negligible. The first objective of this project is to characterize when this occurs (and, importantly, when it does not) in the relatively simple setting of an observational study with many potential confounding variables, and develop Bayesian methods which can be proven theoretically to be robust to dogmatism. The second objective of this project is to extend the insights obtained from the first objective to more advanced designs, such as adaptive clinical trials and observational studies with time-varying treatments and confounding variables. The final objective of this project is to begin the development of a comprehensive computational platform for implementing Bayesian nonparametric causal inference which allow for both (i) careful control over prior specification for important causal parameters and (ii) in-depth sensitivity analysis to assess robustness to untestable causal assumptions.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。在机器学习和人工智能最近取得的巨大成功的推动下，近年来人们对使用机器学习来阐明重要的政策问题产生了浓厚的兴趣。这类问题的例子包括“这种疾病的治疗是否会改善特定患者的生活质量？“以及“参加这个学术项目会提高学生的成绩吗？“在过去十年中，应用最先进的预测算法来为政策提供信息受到了极大的关注，计量经济学家，统计学家和计算机科学家做出了贡献。尽管机器学习取得了成功，但也存在从业者不太了解的陷阱。我们认为，机器学习表面上的灵活性间接导致了一种刚性，其结果是，分析的结果可能是一个已成定局的结论，只由选择使用灵活的模型，而不是任何经验数据驱动。例如，这是一个常见的流行的说法，“相关性不是因果关系”，人们必须警惕的共同原因，可以解释一个明显的因果关系;然而，我们表明，设计不良的机器学习方法的行为很像人类做的，并在某种意义上偏向于归因于因果关系的相关性。该提案的总体目标是理解和纠正基于贝叶斯推理的特定类型算法背后的隐藏假设，并基于这些见解开发强大的方法，并开始发展一个总体计算框架，以实施政策-贝叶斯机器学习的吸引力在于，它承诺将贝叶斯机器学习的预测准确性与现代机器学习和贝叶斯推理的原则性不确定性量化。然而，许多问题的先验规范的间接性质导致了一种我们称为先验教条主义的现象：由于高维空间上独立先验的固有属性，设计不良的贝叶斯模型可能会对混杂量可以忽略不计的假设表现出极端的偏见。该项目的第一个目标是在具有许多潜在混杂变量的相对简单的观察性研究环境中描述这种情况何时发生（重要的是，何时不发生），并开发贝叶斯方法，该方法可以在理论上证明对教条主义具有鲁棒性。该项目的第二个目标是将从第一个目标获得的见解扩展到更先进的设计，例如适应性临床试验和具有时变治疗和混杂变量的观察性研究。本项目的最终目标是开始开发一个全面的计算平台，用于实现贝叶斯非参数因果推理，该平台允许（i）仔细控制重要因果参数的先验规范，以及（ii）深入的敏感性分析，以评估不可测试的因果假设的鲁棒性。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的学术价值和更广泛的影响审查标准。