[mlthoughts2018]

My gripe isn't with the importance of what Pearl published-- of course it's important. I just mean the concept of conditioning on how the target or observational outcome varies when you intentionally vary some conditional variables, that concept for use in machine learning is not new at all. Causal models would just be one more take on it, with interconnections and differences and pros and cons compared with what came before. But it's always disingenuously framed like, "ML practitioners never knew about doing this, but it's the only way to truly go further with our models."