Causal graphs and graph-based causal reasoning (using graphs to select good controls, mediation analysis, the front-door criterion, etc...) are still not mainstream in econometric research. About 10 years ago Guido Imbens (Nobel prize for Economics 2021) debated with Judea Pearl (Turing award 2011) about the topic, arguing that Rubin's potential outcome framework is more convenient for the typical problems faced by economists:
As late as 2019, Andrew Gelman was basically echoing this view for statistics in general: https://statmodeling.stat.columbia.edu/2019/06/18/causal-inference-i-recommend-the-classical-approach-an-observational-study-is-understood-in-reference-to-a-hypothetical-controlled-experiment/
Will the causal revolution finally come to econometrics, based on my judgment of the community's consensus, by Dec 31, 2028?
1,000most econs still use stata, a proprietary programming language that can barely handle multiple datasets in memory. gonna go with no here
(btw "causal revolution" has already occured in econ, at least in terms of how economists call it. the fields "causal revolution" was when angrist, card, krueger, etc. started pushing for identification of causal impacts in like 1990s-2000s)