Causal inference, a field I've been studying for a few months, is often touted as achieving this. That's not quite true -- the causes do not live in the data.

Causal inference, a field I've been studying for a few months, is often touted as achieving this. That's not quite true -- the causes do not live in the data. What causal inference does is estimate the parameters of a given causal model, and does so by applying algorithms on the model to determine which variables to control for. Once the researcher to supply directions of causality, you can use the data to validate the model, or ask questions about it that you already know the answer to as a cross check. But there can be multiple valid models, and the data alone cannot help you select the "right" one. Worse, sometimes (often?) validating the "right" model requires data you do not have access to. But you don't need to do the experiment to find that out at least! Underlying all this appears to be Bayesian methods. Which makes sense as you are often seeking answers to conditional probabilities, Bayes is good for that.