Here is an interesting bit of intellectual history. In his 2000 book “Causality”, Judea Pearl describes how he got to the initial idea that sparked the development of causal inference based on directed acyclic graphs.
As you can see, the idea of thinking about interventions in quasi-deterministic systems is strongly rooted in econometrics (so no need for the not-invented-here-syndrome). In a sense, this story is also typical for the modern literature on machine learning, where smart computer scientists discover (some say “reinvent”, but that’s too disparaging for my taste) approaches from statistics and econometrics and take them to the next level. Because of his prior, Turing-award-worthy work on Bayesian networks, Pearl was able adapt the idea of structural causal models and equip it with a powerful symbolic language that allows us to solve problems far beyond what has been possible with traditional econometric techniques. Clearly, we have much to learn from each other and can only benefit from the convergence of interest from both disciplines.
If you want to know more about the history of graphical causal models and some of the amusing anecdotes around their origin (involving for example guinea pigs, but I shouldn’t spoiler), I can highly recommend you Pearl’s newest book “The Book of Why”, written together with Dana Mackenzie. It’s both an easily accessible introduction to the topic as well as an entertaining account of the last 25 years of Pearl’s research. On top of that you get some more funny rants about Karl Pearson—“causality’s worst adversary”. Definitely worth a read! :)