Statistical inference using machine learning methods

机器学习领域为我们提供了一系列强大的预测方法，其中包括随机森林、增强树、核机器和神经网络等，在各种应用中都取得了极大的成功。然而，在许多情况下，获得预测并不是唯一的目标，但我们希望了解哪些变量是重要的，它们如何影响反应，并量化我们结论的不确定性。虽然这些任务的推理工具很容易用于标准统计方法，但这些方法通常依赖于不切实际的建模假设。这个项目将开发旨在利用机器学习方法的预测能力的方法，以提供这种不受经典模型限制的推理分析。

The field of machine learning has given us an array of powerful prediction methods with random forests, boosted trees, kernel machines and neural networks, among others, being highly successful in a variety of applications. There are however many settings where obtaining predictions is not the only goal, but we would like to understand which variables are important and how they influence the response, and also quantify the uncertainty of our conclusions. Whilst the inferential tools for these tasks are readily available for standard statistical methods, these typically rely on unrealistic modelling assumptions. This project will develop approaches that aim to harness the predictive ability of machine learning methods to provide this sort of inferential analysis without the restrictions of classical models.