Causal Inference and Machine Learning Methods

因果推理和机器学习方法

• 批准号: 1941419
• 负责人: Rajarshi Mukherjee
• 金额: $ 12.07万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941419&HistoricalAwards=false
• 关键词: Causal; Inference; Machine; Learning; Methods

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.

因果推理是统计研究的一个广泛领域，研究人员对定量探索暴露与结果之间的因果关系感兴趣。该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了解疾病治疗的功效、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，以及精心植入的产品在调节市场行为中的作用。统计范式的如此巨大的广度自然也伴随着它的微妙之处和陷阱。对因果关系进行统计学原则分析的主要挑战之一是存在其他因素，即混杂因素，这些因素常常误导调查人员，错误地相信虚假关系。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增强，使得在因果推理研究的常见例子中测量许多此类因素成为可能。尽管从原则上讲，这使得因果推理成为一个更加可行和令人兴奋的领域，但此类研究的数学形式主义仍然带有处理这些混杂因素的假设负担——这往往对实际应用产生极大的限制。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。该项目旨在了解这些工具在以统计原理和数学上合理的方式解开因果推理相关问题方面的作用。如上所述，人们经常认为，使用机器学习方法来非参数估计有害参数可以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问题中获得较低的预测误差，而对因果效应等数量的估计可能需要有所不同的理解。因此，该项目旨在理清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分为以下几个方面：(i) 探索对正式统计方法的关键且经常被忽视的需求，以推断因果效应，这些方法适用于实践中所做的标准假设，(ii) 因果关系中介分析框架为最先进的机器学习方法的无缝应用铺平了道路，以及（iii）在这些推理问题的背景下对深度神经网络和生成对抗网络等机器学习算法的数学探索。所形成的理解将用于探索生命早期接触金属混合物（例如通过饮用水接触铅、砷和镉）对晚年神经系统疾病（例如阿尔茨海默病）的影响以及高维生物标志物的潜在作用该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning

关于机器学习估计的因果参数的名义置信区间覆盖的近无假设检验

• DOI: 10.1214/20-sts786
• 发表时间: 2019-04-08
• 期刊: Statistical Science
• 影响因子: 5.7
• 作者: Lin Liu;R. Mukherjee;J. Robins
• 通讯作者: J. Robins