CRII: RI: New Methods for Learning to Personalize from Observational Data with Applications to Precision Medicine and Policymaking

个性化长期以来一直是机器学习的核心问题，在新闻和产品推荐中有成功的应用，其中训练个性化推荐模型通常是基于重复的廉价实验。一个日益重要的问题是，如何将这种成功转化为精准医疗等新兴问题，在精准医疗中，个性化似乎是关键。该项目将开发新的、强大的方法，以坚实的理论为后盾，解决个性化及其在精准医疗和政策制定中的应用日益紧迫的问题。此外，该项目本身将调查精准医疗和政策制定方面的应用，目的是制定具体的指导方针，供从业者遵循。更广泛地说，这项研究将在机器学习和因果关系的交叉点上取得进展，这反过来又将促进我们对大规模数据决策的理解。在精准医学中，作为本研究的一部分而开发的方法将通过统计有效地学习基于人口统计学和遗传特征的最佳个性化方法，从而改善患者的治疗效果。这项研究还对政策制定产生了影响，在政策制定中，个性化可以用于有针对性的教育干预，提高旨在减少累犯的项目的成功率，从而降低监禁率和矫正费用。新的个性化方法的实现将作为R和Python的免费开源软件包发布。这些软件包将为任何医生、社会学家和其他科学家或从业者提供一个完整的工具集，为他们的应用开发高度有效的个性化模型，仅基于观察数据。研究工作包括培训和指导研究生和本科生，重点是与该领域代表性不足的群体接触。研究成果将在公共论坛上传播，包括以多样性为重点的场所，以提供额外的外联机会。与被动的数据收集相比，医学和相关领域的实验可能规模小、成本高、危险和/或不道德。幸运的是，大量且不断扩展的数据集是可用的，包括医院的电子医疗记录，以及越来越多的基因分型实践提供的越来越丰富的数据。然而，这些数据集纯粹是观察性的，非实验性的，其中特定治疗的孤立因果效应被无数混淆因素所掩盖，需要仔细挖掘。因为，事实证明，基于预测分析的标准方法在这种情况下是不够的，这就产生了一个迫切重要的方法论问题，即如何使黑盒机器学习的成功适应于学习如何根据完全观察数据个性化治疗以获得最大因果效应的规定目的。本研究项目的目的是通过开发个性化理论、方法和应用，努力推进当前的机器学习方法，以应对这一新兴挑战。个性化是机器智能理论和应用的核心。在过去十年中，学习个性化的问题一直是一个令人兴奋的研究领域，主要关注web服务的协同过滤和推荐应用程序。与此同时，在机器学习社区中，从观察数据和医学应用中进行因果推理的兴趣都有了巨大的增长。这项研究将推动机器学习和因果推理的发展，并加强机器学习、因果推理、个性化和医学之间的联系。

Personalization has long been a central problem in machine learning with successful applications in news and product recommendation, where training personalized recommendation models is usually based on repeated cheap experiments. A question of growing importance is how to translate this success to emergent problems such as precision medicine, where personalization appears to be key. The project will develop new and powerful methods, backed up by solid theory, to address increasingly urgent problems in personalization and its applications to precision medicine and policymaking. Moreover, the project will itself investigate applications to precision medicine and policymaking with an aim of developing specific guidelines that can be followed by practitioners. More generally, the research will lead to progress at the intersection of machine learning and causality, which in turn will advance our understanding of decision making from large-scale data. In precision medicine, the methods developed as part of this research will lead to improved patient outcomes through statistically efficient learning of the best way to personalize based on demographic and genetic characteristics. The research also has impact on policymaking, where personalization can be used to target educational interventions and improve the success of programs aimed at reducing recidivism, which in turn will reduce rates of incarceration and corrections spending. Implementations of the new personalization methods will be distributed as free, open-source packages for R and Python. These packages will provide a complete toolset for any doctor, sociologist, and other scientist or practitioner to develop highly effective personalization models for their application based solely on observational data. The research effort includes training and advising graduate and undergraduate students, with an emphasis on engaging with groups under represented in the field. Research results will be disseminated in public fora, including diversity-focused venues that offer an added outreach opportunity.Medicine and related contexts have the property that experimentation can be prohibitively small-scale, costly, dangerous, and/or unethical, in comparison to passive data collection. Luckily, massive and ever expanding datasets are available, including hospitals' electronic medical records, with richer and richer data available from increased genotyping practices. However, such datasets are purely observational and non-experimental, where the isolated causal effect of a particular treatment is hidden by a myriad confounding factors and needs to be carefully mined out. Since, as it turns out, standard approaches to the problem based on predictive analyses fall short in this setting, this gives rise to urgently important methodological questions as to how to adapt the success of black-box machine learning to the prescriptive purpose of learning how to personalize treatments for maximal causal effect based on completely observational data. The purpose of this research project is to work toward advancing current machine learning methodology to step up to this emerging challenge by developing personalization theory, methods, and applications. Personalization is at the core of machine intelligence theory and applications. The problem of learning to personalize has been an exciting area of research over the last decade, with a strong focus on collaborative filtering and recommendation applications for web services. At the same time, among the machine learning community, there has been a tremendous growth of interest both in causal inference from observational data and in medical applications. Work on the research will result in advances in machine learning and causal inference and in stronger connections between machine learning, causal inference, personalization, and medicine.