Journal article

Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference

Tony Blakely, John Lynch, Koen Simons, Rebecca Bentley, Sherri Rose

INTERNATIONAL JOURNAL OF EPIDEMIOLOGY | OXFORD UNIV PRESS | Published : 2020

Abstract

Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought 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 useful perspective 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 propens..

View full abstract

Grants

Awarded by Health Research Council of New Zealand Programme Grant


Awarded by Australian Research Council (ARC) Future Fellowships


Awarded by NHMRC Centre of Research Excellence


Awarded by NIH Director's New Innovator Award


Funding Acknowledgements

T.B. was supported by a Health Research Council of New Zealand Programme Grant (16/443). R.B. was funded by Australian Research Council (ARC) Future Fellowships (FT150100131). J.W.L. was supported by an NHMRC Centre of Research Excellence (GNT1099422). S.R. was supported by an NIH Director's New Innovator Award (DP2-MD012722).