Causal Inference and Machine Learning Methods

因果推断是统计研究的一个广泛领域，研究人员对暴露和结果之间因果关系的定量探索感兴趣。这个框架下的问题涉及广泛的应用领域，包括医学和生物学、经济学、社会科学和环境健康。 具体的例子可以包括理解疾病治疗的效果，基因和蛋白质对决定生物功能的重要性，环境因素和遗传变异对人类死亡率的相互作用，以及精心的产品植入对调节市场行为的作用。统计范式的这种巨大广度自然伴随着它的微妙之处和陷阱。因果关系的统计原则分析的主要挑战之一是其他因素的存在，称为混杂因素，这些因素经常误导调查人员错误地相信虚假的关系。因此，考虑这些混杂因素变得非常重要。人类收集越来越多数据的能力不断提高，使得在因果推理研究的常见例子中对许多此类因素进行测量成为可能。虽然在原则上，这使得因果推理成为一个更可行和令人兴奋的领域，但这些研究的数学形式主义仍然带有处理这些混淆因素的假设负担-这通常对实际应用具有极大的限制性。人们普遍认为，机器学习和人工智能工具是减轻这些假设负担的自然选择。这个项目的目的是了解这些工具的作用，在解开因果推理相关的问题，在统计原则和数学健全的方式。如上所述，人们经常认为，使用机器学习方法来非参数化地估计讨厌的参数会减轻观察研究中所做假设的负担。虽然本质上是正确的，但大多数机器学习方法都是为了在回归类型的问题中实现低预测误差，而对因果效应等数量的估计可能需要不同的理解。因此，该项目旨在解开因果效应研究中使用的一些机器学习算法。该项目的主要目标可以分为以下几个方面--（i）探索关键的、经常被忽视的、用于推断因果效应的正式统计方法的需求，这些方法可以适应实践中的标准假设，（ii）因果中介分析框架，为无缝应用最先进的机器学习方法铺平道路，以及（iii）在这些推理问题的背景下对机器学习算法（如深度神经网络和生成对抗网络）进行数学探索。发展的理解将用于探索早期生活暴露于金属混合物的影响（如铅、砷、和通过饮用水接触镉）对老年神经系统疾病的影响（如老年痴呆症）以及可能调节这种效应的高维生物标志物（如EV miRNA）的潜在作用。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学术价值和更广泛的影响审查标准。

Causal Inference is a broad area of statistical research where investigators are interested in the quantitative exploration of cause and effect relationships between exposures and outcomes. Questions that fall under this framework range across a vast canvass of applications, including, medical sciences and biology, economics, social sciences, and environmental health. Specific examples can include understanding the efficacy of treatments for a disease, the importance of genes and proteins on deciding biological functions, the interplay between environmental factors and genetic variations on human mortality, and the role of careful product placements to modulate market behaviors. This immense breadth of the statistical paradigm naturally comes with its share of subtleties and pitfalls. One of the major challenges in a statistically principled analysis of cause and effects is the presence of other factors, known as confounders, which often mislead investigators in falsely believing spurious relationships. Accounting for such confounders, therefore, becomes of great importance. The increasing ability of human beings to collect more and more data has made measurements of many such factors possible in common examples of causal inference studies. Although in principle, this has made causal inference a more feasible and exciting field, the mathematical formalism of such studies still come with a burden of assumptions to deal with these confounders -- which can often be extremely restrictive for practical applications. It is widely believed that tools from machine learning and artificial intelligence are natural choices to alleviate the burden of these assumptions. This project is aimed at understanding the role of these tools in disentangling causal inference related questions in a statistically principled and mathematically sound manner. As mentioned above, it is often argued that the use of machine-learning methods to nonparametrically estimate nuisance parameters alleviates the burden of the assumptions made in observational studies. Although true at heart, most machine learning methods are geared to attain low prediction errors in regression type problems -- whereas estimation of quantities like causal effects might require a somewhat different understanding. This project is, therefore, aimed at disentangling some machine learning algorithms used in the study of causal effects. The major goals of this project can be divided into the following regimes -- (i) exploring the crucial, and often overlooked, need of formal statistical methods for inferring causal effects which are adaptive over standard assumptions made in practice, (ii) a causal mediation analysis framework which paves the way for seamless application of state of the art machine learning methods, and (iii) the mathematical exploration of machine learning algorithms such as deep neural networks and generative adversarial networks in the context of these inferential problems. The developed understanding will be used to explore the effect of early life exposure to metal mixtures (like lead, arsenic, and cadmium exposures through drinking water) on late-life neurological diseases (such as Alzheimer's disease) and the potential role of high dimensional biomarkers such as EV miRNA's that might modulate such effects.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.