This is in response to a post in which the necessity of causal inference is debated, and an example where causal formalism comes into play is given (as related to studying the effect of antidepressants on weight in the presence of censoring).

At one time, causal inference was called “observational data analysis.” I wonder if it would be helpful to revert back to this.

The antidepressant example is nice – formalism does not create complexity, it reveals it. However, it is notable that finding an example that does not involve unmeasured confounding requires that one reach outside of what would typically be considered observational data analysis…

Overall, I think the major issue is that causal inference when considered a “tool” for observational data is misleading, as it makes people think it’s achievable, when really the unobserved confounder assumption is a major bane. This said, as I think is shown, the potential outcomes notation (and do-calculus) are not “tools” for inference, they are ways of describing what already is.

Further, although the no measure confounders assumption is formidable, often it’s a trade-off between making it and not doing an analysis at all. Sometimes better to do the analysis, especially when one can’t randomize (eg, tobacco use).

Overall, though, I wonder if a rose by another name might smell as sweet.