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

该项目属于EPSRC数学科学研究领域的福尔斯。 因果推断中的外推是指对观察到的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有效性。这是因果推断中治疗效果估计的一个关键方面，因为现实世界的应用通常需要进行超出可用数据范围的推断。它涉及对观察到的和推断出的背景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倚。 常见的挑战包括基线特征的差异，未测量的混杂因素，以及观察和外推背景之间治疗反应的变化。虽然在这个大数据时代，机器学习已经在足够的数据下显示出令人印象深刻的预测性能，但性能通常不稳定，使其贡献不可靠。缺乏一个强大的机器学习模型，能够外推和转移对现有数据的学习到目标人群是存在和交织。 我们的目标是建立一个强大的因果推断理论，通过结合机器学习和统计来解决上述所有问题。我们将提供可扩展的方法，外推以及严格的理论证明不确定性量化。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。我们希望将理论应用于实践，我们的方法可以促进临床试验设计和治疗效果识别/估计。这将通过首先彻底理解在这种情况下临床试验决策的简单现象来完成。总之，当研究人员的目标是应用超出其观察数据范围的因果效应估计时，因果推断治疗效应估计中的外推是必不可少的。虽然它可能具有挑战性，需要仔细考虑假设和验证，但设计良好的外推方法可以增强因果推理结果在各个领域的适用性和普遍性，包括医疗保健，社会科学和政策分析。

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.