Here are answers to questions I frequently encounter

Wen Sun's course:

  This is a fantastic resource to learn the fundamentals of reinforcement learning.  

Sergey Levine's course:

This is a fantastic resource for learnning deep reinforcement learning.

Causal inference I am referring to here is more statistical aspects (i.e., semiparametric efficiency, doubly robust estimators, offline policy learning, IV, DID, RDD, etc.). I strongly recommend this book 

Applied Causal Inference Powered by ML and AI by V. Chernozhukov, C. Hansen, N. Kallus, M. Spindler, V. Syrgkanis

 I also learned a lot from the materials from Andrea Rotnitzky and Vasilis Syrgkanis. It looks like these materials are not in public 

+ Causality roughly consists of (1) causal inference, (2) causal identification (i.e., like ID algorithm), and (3) causal discovery. I am not familiar with (3). Regarding (2), I have learned a lot from papers by Elias Barenboim and Ilya Shpitser. But, again, I am not sure about a very instructive material that is good for beginners. 

    Of course, you don't need to know all of the stuff below to start research. Definitely, attempting to learn everything is overkill (at least, I still don't fully grasp many parts yet). But, it would be fun and meaningful to learn gradually. 


High-Dimensional Statistics by Martin J. Wainwright: Bible of statistical learning theory.  

Asymptotic statistics by  A. W. van der Vaart:  Bile of mathematical statistics. Learning this might be a bit tough in the era of deep learning. There are many distractions in the world now:) But, I have learned a lot from it.

Bandit algorithms by Tor Lattimore and Csaba Szepesvári: Bible of bandit/online learning

Introduction to Online Convex Optimization by Elad Hazan: This is short; but extremely insightful and organized material for online learning

    (Cousres online)

     A course offered by Ryan Tibshirani

     High-Dimensional Statistics