I completely agree that you can’t infer causality by “passively” looking at data, in the sense that it sounds like what you’re describing is naively looking at a scatter plot or running a regression of Y on X.

The key insight of causal econometrics is exactly the point you’re making, that in order to understand causality we have to somehow approximate the environment that is present in a lab, where we can randomly assign individuals to treatment and control groups, ensuring that people in both groups are on average the same and thus the only difference in expectation between these groups is the treatment of interest. Of course, we can’t do this with observational data, so we look for natural experiments, or environments where random distribution of treatment may occur among some population by chance.

There are a lot of specific methods, but the essence of them all is that, as long as there is some feature that is as-good-as randomly distributed between people, and that feature is correlated with the treatment we care about, we can use variations in that random factor to estimate the causal effect of changing treatment for those individuals who shift their behavior because of the random variable. An early example in economics is using variation in military participation driven by the Vietnam draft lottery to estimate the causal effect of military participation on lifetime earnings. So in that way, economists really do try to estimate causality by looking for situations in which we might think the “cause” knob is being turned due to historical or institutional quirks.

I’m just skimming the surface, but if you’re interested you should check out Mostly Harmless Econometrics by Angrist and Pischke (the former of whom won the Nobel Prize last year for these findings) or Causal Inference: The Mixtape. Our capability of being very confident about causality in data is definitely limited to when we can find these “natural experiments,” but researchers have been able to find quite a lot and it really forms the basis of modern empirical economics research.

I’m getting my phd in economics with a focus on empirical estimation, and I want to offer some support for your article as well as some perspective from one of the disciplines you cited in the post. In general, I think statements of causality like the Brooks piece you cited are incredibly misleading and entirely unprovable, usually marred by reverse causality and the ommission of other potential explanatory variables. I totally agree that any causal statements, especially those made by political actors, should be viewed with skepticism.

But as far as causal claims within academic research, I do believe economics takes it more seriously than other non-quantitative disciplines. Econometrics, the economic sub-discipline of statistics, is almost chiefly concerned with understanding when we can say that statistical estimates can be interpreted as causality. In the last few decades, researchers have become even more precise in their understanding of what types of causality we’re measuring (I.e. what portion of the population it’s relevant to). In general, we’re considering the causal effects of policies or behavior rather than causation of events in history, which is, as you suggest, nearly impossible to parse in most scenarios. Our reach can certainly be limited and we don’t get it right 100% of the time, but every economist I know does not make causality claims lightly. Perhaps this reveals a bit of my personal bias for a discipline that I am fascinated by, and nothing you’re saying here directly implies that you disagree with anything I’ve written here, but I wanted to add a bit of my own perspective from someone who feels we can all be a bit too quick to state two things are connected by causality and spends most of their time trying to figure out when we can actually make those claims. Great article! :)