Reflection on modern methods: When worlds collide - Prediction, machine learning and causal inference

Abstract

Causal inference requires theory and prior knowledge to structure analyses, and is not usuallythought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a usefulperspective on contemporary causal inference methods, and (ii) explore the role of machine learning(as one approach to 'best prediction') in causal inference. Causal inference methods covered include propensity..