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。 因果推断的推断是指对观察到的数据范围以外的情况或环境进行预测或估计因果效应的过程。它允许研究人员概括从特定研究获得的治疗效应估计值，以对新的或不同的环境。例如，如果临床试验评估了药物在特定患者人群中的有效性，则研究人员可能希望推断结果以评估药物在不同人群中的功效。这是因果推断中治疗效应估计的关键方面，因为现实世界的应用通常需要推断出超出可用数据范围。它涉及对观察到的和推断的上下文之间的相似性做出假设。这些假设可以将不确定性和潜在偏见引入估计的治疗效果中。 共同的挑战包括基线特征，未衡量的混杂因素以及观察到的和推外环境之间治疗反应的变化。尽管在这个大数据时代，机器学习表现出了其在预测性能中具有足够数据的令人印象深刻的能力，但性能通常是不稳定的，因此其贡献不可靠。缺乏强大的机器学习模型能够推断和转移现有数据向目标人群进行转移学习。 我们旨在建立一种因果推断中强大的外推理论，以通过嫁给机器学习和统计数据来解决上述所有问题。我们将提供可扩展的方法，这些方法可以很好地推断出不确定性定量的严格理论证明。与我们的行业合作伙伴（诺华）合作，我们在数据收集和应用程序方案识别方面取得了一些进展。我们希望将理论带入我们的方法可以促进临床试验设计和治疗效果识别 /估计的情况下实践。这将是通过首先了解在这种情况下对临床试验决策的更简单现象的彻底了解来完成的。总而言之，当研究人员旨在应用其观察到的数据范围之外的因果效应估计值时，因果推理治疗效果估计是必不可少的。尽管这可能是具有挑战性的，并且需要仔细考虑假设和验证，但精心设计的外推方法可以增强因果推理发现在包括医疗保健，社会科学和政策分析在内的因果推理发现的适用性和概括性。