CAREER: Foundations for Bayesian Nonparametric Causal Inference

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。近年来，在机器学习和人工智能取得巨大成功的推动下，人们对利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“这种疾病的治疗方法会提高某个特定病人的生活质量吗？”和“参加这个学术项目会提高学生的成绩吗？”在过去的十年里，计量经济学家、统计学家和计算机科学家都对应用最先进的预测算法来制定政策做出了巨大的贡献。尽管机器学习取得了成功，但仍存在从业者尚未充分理解的陷阱。我们认为，机器学习的明显灵活性间接导致了一种僵化，其结果是，分析结果可能是预料之中的结论，仅由选择使用灵活的模型而不是任何经验数据驱动。例如，“相关性不是因果关系”是一种常见的流行说法，人们必须警惕可以解释明显因果关系的共同原因；然而，我们表明，设计不良的机器学习方法的行为与人类的行为非常相似，并且在某种意义上倾向于将相关性归因于因果关系。本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，基于这些见解开发强大的方法，并开始开发一个总体计算框架，用于在实践中实施面向策略的贝叶斯机器学习方法。贝叶斯机器学习的吸引力在于，它有望将现代机器学习的预测准确性与贝叶斯推理的原则性不确定性量化结合起来。然而，对于许多问题，先验规范的间接性质导致了一种我们称之为先验教条主义的现象：由于高维空间上独立先验的固有特性，设计不良的贝叶斯模型可能会对混淆量可以忽略不计的假设表现出极端的偏差。该项目的第一个目标是在一个具有许多潜在混淆变量的相对简单的观察研究环境中，描述这种情况何时发生（以及，重要的是，何时没有发生），并开发贝叶斯方法，该方法可以在理论上证明对教条主义是稳健的。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。该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前规范，以及（ii）深入的敏感性分析，以评估对不可测试的因果假设的稳健性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.