Statistical inference using machine learning methods

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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.