What are the pros and cons of employing LASSO for causal analysis? Statistical Learning and its results are currently pervasive in Social Sciences. A couple of months ago, Guido Imbens said: "LASSO is the new OLS". 
I  studied Machine Learning a little bit, and I know that its main goal is prediction. I also agree with Leo Breiman's distinction between two cultures of statistics. So, from my point of view, causality is opposed to prediction to some extent.
Considering that sciences usually try to identify and understand causal relations, is machine learning useful for this goal? In particular, what are the advantages of LASSO for causal analysis?
Are there any researchers (and papers) addressing those questions?
 A: I don't know all of them, I'm sure, so I hope no one will mind if we do this wiki-style.
One important one though is that the LASSO is biased (source, Wasserman in lecture, sorry), which while acceptable in prediction, is a problem in causal inference. If you want causality, you probably want it for Science, so you're not just trying to estimate the most useful parameters (which happen strangely to predict well), you're trying to estimate the TRUE(!) parameters.
A: I guess 6 years later, I can understand perfectly the answer to my own question.  Suppose a causal model with $Y$ being an effect of $X$ and other covariates $L$.
LASSO will help someone to come up with a estimate for $E[Y|X, L_1, L_2, ...]$. If one proves that $E[Y|do(X)] = E[Y|X, L_1, ... ]$, then it's alright. Not the case always.
A common case is that $E[Y|do(X)] = E_{L_1,...}[E[Y|X,L_1, ...]]$ if L_1, L_2,... are observed confounders and backdoor adjustment is sufficient for identification. In this case, one could use something like g-formula over LASSO, and then they can estimate the causal effect.
Of course, this will always depend on the proof of identifiability. Estimation $\neq$ identifiability. LASSO is a model for estimation.
