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Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health

Alex Broadbent^1,2, Thomas Grote³

¹Department of Philosophy, Durham University, Durham, England.

Philosophy & Technology

|March 7, 2022

Summary

Machine learning (ML) and epidemiology clash over causation. This paper argues for cautious ML application in public health, suggesting it can discover new concepts without strict causal inference constraints.

Keywords:

Causal inference Epidemiology Explainable AI Intervention Machine learning Opacity Prediction Public health Scientific discovery

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Area of Science:

Epidemiology
Machine Learning
Causal Inference
Public Health

Background:

Epidemiology prioritizes causal inference, while machine learning (ML) research often does not.
A debate exists on whether ML in epidemiology should be causally constrained.
This paper examines the necessity of causal knowledge for predicting public health intervention outcomes.

Purpose of the Study:

To analyze the necessity of causal knowledge for predicting public health intervention outcomes.
To explore motivations for imposing causal constraints on ML in epidemiology.
To argue for a cautious approach to integrating ML in epidemiology.

Main Methods:

Disambiguation of motivations for causal constraints (definitional, metaphysical, epistemological, pragmatic).
Argumentation for a 'Proceed with caution' stance.
Exploration of ML's potential for discovering new epidemiological concepts.

Main Results:

Conviction of impossibility does not justify forbidding attempts.
All explored motivations for causal constraints support a 'Proceed with caution' approach.
ML can facilitate the discovery of new concepts through a process of reflective equilibrium.

Conclusions:

Causal inference can enforce pre-existing classifications, potentially hindering discovery.
ML, when not causally constrained, offers a method for discovering meaningful new concepts in epidemiology.
A cautious, iterative approach integrating ML can advance public health research.