This eloquent exposition of why clinicians are necessary to data science feels like a manifesto for the lab.
We argue that a failure to adequately describe the role of subject-matter expert knowledge in data analysis is a source of widespread misunderstandings about data science. Specifically, causal analyses typically require not only good data and algorithms, but also domain expert knowledge.
And a general critique of ML as a method to improve health
A goal of data science is to help make better decisions. For example, in health settings, the goal is to help decision-makers—patients, clinicians, policy-makers, public health officers, regulators—decide among several possible strategies. Frequently, the ability of data science to improve decision making is predicated on the basis of its success at prediction. However, the premise that predictive algorithms will lead to better decisions is questionable.
And why the human orrery is a dangerous myth
the distinction between prediction and causal inference (counterfactual prediction) becomes unnecessary for decision making when the relevant expert knowledge can be readily encoded and incorporated into the algorithms. For example, a purely predictive algorithm that learns to play Go can perfectly predict the counterfactual state of the game under different moves, and a predictive algorithm that learns to drive a car can accurately predict the counterfactual state of the car if, say, the brakes are not operated. Because these systems are governed by a set of known game rules (in the case of games like Go) or physical laws with some stochastic components (in the case of engineering applications like self-driving cars),
Or more specifically …
…contrary to some computer scientists’ belief, “causal inference” and “reinforcement learning” are not synonyms. Reinforcement learning is a technique that, in some simple settings, leads to sound causal inference. However, reinforcement learning is insufficient for causal inference in complex causal settings