Causal inference has been a huge issue in statistics and its subject fields for a while, see DAG, instruments, Regression discontinuity, RCT, natural experiment

Causal inference has been a huge issue in statistics and its subject fields for a while, see DAG, instruments, Regression discontinuity, RCT, natural experiments... For the main guys, the question is not that causal inference is important and traditional research design is problematic. The issue is with all these big ego guys. For example, Pearl pretty much states that DAG are the only way and his conception is perfect and everyone who disagrees doesnt know statistics (even though DAG can not capture all models of causal inference). Then Angrist and Pischke come and say everyone using parametrized designs, including Pearl, is not believable and its RCT logic or bust. And Gelman fight against everyone anyway. Much of this is less academic dispute than internet blogfighting, sadly. The issue is that in practice, we need to do something with imperfect observational data. Its really easy to critique any study on identification and endogeneity. Its difficult to solve the issue!