Development of robust statistical and machine learning algorithms for extrapolation in causal inference

开发用于因果推理外推的稳健统计和机器学习算法

• 批准号: 2740759
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2740759
• 关键词: Development; robust; statistical; machine; learning

This project falls within the EPSRC mathematical sciences research area. Extrapolation in causal inference refers to the process of making predictions or estimating causal effects for situations or contexts that lie outside the observed data range. It allows researchers to generalize treatment effect estimates obtained from a particular study to new or different settings. For example, if a clinical trial evaluates a drug's effectiveness in a specific patient population, researchers may want to extrapolate the results to assess the drug's efficacy in a different population. This is a crucial aspect of treatment effect estimation in causal inference because real-world applications often require making inferences beyond the scope of available data. It involves making assumptions about the similarity between the observed and extrapolated contexts. These assumptions can introduce uncertainty and potential biases into the estimated treatment effects. Common challenges include differences in baseline characteristics, unmeasured confounders, and variations in treatment response between the observed and extrapolated contexts. Although in this big data era machine learning has shown its impressive capability in predictive performance with sufficient data, the performance is usually unstable, making its contribution not reliable. The lack of a robust machine learning model being able to extrapolate and transfer learning on existing data to target population are present and interweave. We aim to build a theory of robust extrapolation in causal inference to address all the above questions by marrying machine learning and statistics. We will deliver scalable methods that extrapolate well, with rigorous theoretical proof on uncertainty quantification. Collaborating with our industry partners (Novartis), we have made some progress on data collection and application scenario identification. We expect to bring theory to practice where our method can facilitate clinical trial design and treatment effect identification / estimation. This will be done by first getting a thorough understanding of the simpler phenomenon of clinical trial decision making in this context. In summary, extrapolation in causal inference treatment effect estimation is essential when researchers aim to apply causal effect estimates beyond the confines of their observed data. While it can be challenging and requires careful consideration of assumptions and validation, well-designed extrapolation methods enhance the applicability and generalizability of causal inference findings in various domains, including healthcare, social sciences, and policy analysis.

该项目属于 EPSRC 数学科学研究领域。 因果推理中的外推是指对观察数据范围之外的情况或背景进行预测或估计因果效应的过程。它允许研究人员将从特定研究中获得的治疗效果估计推广到新的或不同的环境。例如，如果临床试验评估药物在特定患者人群中的有效性，研究人员可能希望推断结果以评估药物在不同人群中的疗效。这是因果推断中治疗效果估计的一个重要方面，因为现实世界的应用通常需要在可用数据范围之外进行推断。它涉及对观察到的上下文和推断的上下文之间的相似性做出假设。这些假设可能会给估计的治疗效果带来不确定性和潜在偏差。 常见的挑战包括基线特征的差异、未测量的混杂因素以及观察到的背景和推断的背景之间的治疗反应的变化。尽管在大数据时代，机器学习在拥有足够数据的情况下展现出了令人印象深刻的预测性能，但其性能通常不稳定，其贡献并不可靠。缺乏能够将现有数据的学习推断和转移到目标人群的稳健的机器学习模型是存在且相互交织的。 我们的目标是建立一种因果推理的稳健外推理论，通过机器学习和统计学的结合来解决上述所有问题。我们将提供可扩展的方法，可以很好地进行推断，并对不确定性量化提供严格的理论证明。我们与行业合作伙伴（诺华）合作，在数据收集和应用场景识别方面取得了一些进展。我们希望将理论付诸实践，我们的方法可以促进临床试验设计和治疗效果识别/估计。这将通过首先彻底了解在此背景下临床试验决策的更简单现象来完成。总之，当研究人员旨在超出观察数据范围应用因果效应估计时，因果推理治疗效应估计中的外推至关重要。虽然这可能具有挑战性，并且需要仔细考虑假设和验证，但精心设计的外推方法增强了因果推理结果在各个领域（包括医疗保健、社会科学和政策分析）的适用性和普遍性。