Textbook recommendations covering machine learning techniques for causal inference? Over the past 15 years there has been progress in adapting machine learning methods for causal inference. For example: targeted learning, double machine learning, causal trees.
Is there a textbook that covers the current range of techniques? I haven't seen anything on Amazon, perhaps there are texts available on other sites? Or will be published soon?
 A: I follow this area pretty closely, but I think this subfield is so new no textbook exists (yet).
However, there are some course videos that are fairly good:

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*Machine Learning & Causal Inference: A Short Course at Stanford (accompanying tutorial)

*Summer Institute in Machine Learning in Economics (MLESI21) at University of Chicago

There is also a nice survey paper:
"Machine learning methods that economists should know about"
by Susan Athey, Guido Imbens in the Annual Review of Economics (link to draft)
A: As dimitriy states, there isn't a singular textbook yet (or at least that I am aware of). However, there are a few textbook materials you can piece together to cover the topics you mentioned.

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*Targeted Learning in Data Science covers super learner (which is a generalized stacking algorithm you would almost always want to use in practice), and targeted maximum likelihood estimation (with a bunch of variations of it). I think this one will be preferred over the other targeted learning book since the one linked above covers the machine learning parts a bit better

*Chapter 18 of Hernan and Robins covers double machine learning.

Unfortunately, I don't have a recommendation for causal trees
A: For most recent work have a look at the conference for Causal Learning and Reasoning (CLeaR) 2022.
If you want to get started with ML and causal inference, I particular recommend (disclaimer: I m one of the co-authors) to look at Kelly, Kong, Goerg (2022) on "Predictive State Propensity Subclassification (PSPS): A causal inference algorithm for data-driven propensity score stratification".  It's a fully probabilistic framework for causal inference by learning causal representations in the predictive state space for Pr(outcome | treatment, features).  See paper for details.
For a ready to go TensorFlow keras implementation see https://github.com/gmgeorg/pypsps with code examples and notebook case studies.
