The famous paper Dorie,2017 shows that BART performs dramatically well in causal inference. In my replication, MSE in BART can be 40% lower than MSE in other machine learning methods.
But all machine learning methods just regress $Y(0) \sim X$ on control group, get a predictor $f_0(X)$, and use it to predict $Y(0)$ on treatment group, similarly regress $Y(1) \sim X$ on treatment group, get a predictor $f_1(X)$, and use it to predict $Y(1)$ on control group, finally use $f_1(x) - y(0)$ (on control group) or $y(1) - f_0(x)$ (on treatment group) to estimate causal effects. It seems like that the accuracy of causal estimation depends on the accuracy of regression $Y \sim X$, so the great performance of BART in causal inference implies that BART also performs great in the general regression problem.
But BART is not very famous in the general regression problem, for example, the citations of Breiman's random forest paper are 50 times more than the citations of Chipman's BART paper. So, is BART just ignored in machine learning, or is BART particularly accurate in causal inference? If BART is particularly accurate, WHY?