Amy Cohen asked me what I thought of this article, “Control of Confounding and Reporting of Results in Causal Inference Studies: Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals,” by David Lederer et al.
I replied that I liked some of their recommendations (downplaying p-values, graphing raw data, presenting results clearly) and I am supportive of their general goal to provide statistical advice for practitioners, but I was less happy about their recommendations for causal inference, which was focused on what taking observational data and drawing causal graphs. Also I don’t think their phrase “causal association” has any useful meaning. A statement such as “Causal inference is the examination of causal associations to estimate the causal effect of an exposure on an outcome” looks pretty circular to me.
When it comes to causal inference, I prefer a more classical approach in which an observational study is understood in reference to a hypothetical controlled experiment.
I also think that the discussion of causal inference in the paper is misguided in part because of the authors’ non-quantitative approach. For example, they consider a hypothetical study estimating the effect of exercise on lung cancer and they say that “Controlling for ‘smoking’ will close the back-door path.” First off, given the effects of smoking on lung cancer, “controlling for smoking” won’t do the job at all, unless this is some incredibly precise model with smoking very well measured. The trouble is that the effect of smoking on lung cancer is so large that any biases in this measurement could easily overwhelm the effect they’d be trying to estimate. And this sort of thing comes up a lot in public health studies. Second, you’d need to control for lots of things, not just smoking. This example illustrates how I don’t see the point of all their discussion of colliders. If we instead simply take the classical approach, we’d start with a hypothetical controlled study of exercise on lung cancer, a randomized prospective study in which the experimenter assigns exercise levels to patients, who are then followed up, etc., then we move to the observational study and consider pre-treatment differences between people with different exercise levels. This makes it clear that there’s no “back-door path”; there are just differences between the groups, differences that you’d like to adjust for in the design and analysis of the study.
Also I fear that this passage in the linked article could be misleading: “Causal inference studies require a clearly articulated hypothesis, careful attention to minimizing selection and information bias, and a deliberate and rigorous plan to control confounding. The latter is addressed in detail later in this document. Prediction models are fundamentally different than those used for causal inference. Prediction models use individual-level data (predictors) to estimate (predict) the value of an outcome. . . ” This seems misleading to me in that a good prediction study also requires a clearly articulated hypothesis, careful attention to minimizing selection and information bias, and a deliberate and rigorous plan to control confounding.
The point is that, once you’re concerned about out-of-sample (rather than within-sample) prediction, all these issues of measurement, selection, confounding, etc. arise. Also, a causal model is a special case of a predictive model where the prediction is conditional on some treatment being applied. So I think it’s a mistake to think of causal and predictive inference as being two different things.