We propose a cutting-edge and transformative paradigm for statistical modelling that is crucial to enhance the quality of data analyses. We will establish the fundamental principles of a comprehensive estimation theory, which maps model parameters onto generic, interpretable, model-free estimands with favourable efficiency bound, and harnesses the power of debiased (statistical/machine) learning techniques to estimate these. Our core objective is to develop a flexible and accessible data modelling framework, which will deliver minimal bias and maximal interpretability, even in the presence of model misspecification, along with honest confidence bounds that account for model uncertainty. In particular, we will develop assumption-lean modelling strategies to tackle significant and timely challenges in causal modelling, including the first coherent analysis framework for 'target trial emulation'.