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?

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.
