Journal
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Volume 49, Issue 6, Pages 2058-2064Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ije/dyz132
Keywords
Machine learning; causal inference; prediction; potential outcomes
Categories
Funding
- Health Research Council of New Zealand Programme Grant [16/443]
- Australian Research Council (ARC) Future Fellowships [FT150100131]
- NHMRC Centre of Research Excellence [GNT1099422]
- NIH Director's New Innovator Award [DP2-MD012722]
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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 propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs.
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