Is handling errors with Random Forest more superior compared to mean or zero imputation?

Hi, I came upon [this post in Linkedin](https://www.linkedin.com/feed/update/urn:li:activity:7121482516829507584?utm_source=share&utm_medium=member_android), in which a guy talks about how handling errors with imputing means or zero have many flaws (changes distributions, alters summary statistics, inflates/deflates specific values), and instead suggests to use this library called "MissForest" imputer to handle errors using a random forest algorithm.

**My question is, are there any reasons to be skeptical about this post?** I believe there should be, since I have not really heard of other well established reference …

books data datascience errors imputation mean missing values random reference speculation values