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。因果推断中的外推是指对观察到的数据范围之外的情景或情境做出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中。例如，如果一项临床试验评估了一种药物在特定患者群体中的有效性，研究人员可能想要推断结果以评估该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做出超出可用数据范围的推断。它涉及到对观察到的上下文和推断出的上下文之间的相似性进行假设。这些假设可能会给估计的治疗效果带来不确定性和潜在的偏差。共同的挑战包括基线特征的差异，未测量的混杂因素，以及在观察和推断的情况下治疗反应的差异。尽管在这个大数据时代，机器学习在拥有足够数据的情况下表现出了令人印象深刻的预测性能，但其性能通常是不稳定的，使得其贡献不可靠。缺乏一个稳健的机器学习模型，能够推断现有数据并将学习传递给目标人群，这一点是存在的，而且是相互交织的。我们的目标是建立因果推理中的稳健外推理论，通过将机器学习和统计学相结合来解决所有上述问题。我们将提供可扩展的方法，这些方法可以很好地进行推断，并在不确定性量化方面提供严格的理论证明。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，使我们的方法能够促进临床试验设计和治疗效果的识别/评估。这将通过首先彻底了解在这种背景下临床试验决策的更简单的现象来完成。总之，当研究人员的目标是将因果效应估计应用于他们观察到的数据的范围之外时，在因果推断治疗效果估计中的外推是必不可少的。虽然这可能是具有挑战性的，需要仔细考虑假设和验证，但设计良好的外推方法提高了因果推理结果在各个领域的适用性和概括性，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.