Delighted to share my book chapter on machine learning and causal inference now available in Wiley StatsRef
https://onlinelibrary.wiley.com/doi/full/10.1002/9781118445112.stat08412
In it, we 1: distinguish between identification and estimation (with machine learning being applicable to esitmation), 2: summarize the challenges of convergence and complexity and solutions, 3: point to various extensions, and 4: conclude with general advice for practical application
This chapter is a more focused version of thoughts I've had that are scattered elsewhere
e.g.
https://pubmed.ncbi.nlm.nih.gov/33591058/
https://stats.stackexchange.com/questions/482445/is-double-machine-learning-doubly-robust-if-so-how/482498#482498