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?

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
