|Causal Inference in Statistics: A Primer||Pearl||Short Introduction||Daniel||A delightfully compact introduction that provides thought-provoking examples for the limitations of (non-causal) statistical inference. The basics of intervention distributions and counterfactuals are introduced up to a level that enables readers to apply the techniques to their own problems.|
|The Book of Why||Pearl||Accessible to broader audience||Daniel||Is written for a broader audience and, albeit not a scientific treatise, introduces a fair share of theory. This is a good first book if you want to understand the central ideas of the field without to many technicalities.|
|Causality||Pearl||Theory, Standard Reference||Daniel||A comprehensive treatment of the subject and the closest thing to a standard reference (cited over 18k times). It is at the same time very (almost excessively) rigorous in presenting the underlying mathematics, while also containing deep conceptual insights about the entailed philosophical consequences of the formalism.|
|Elements of Causal Inference||Janzing, Peters, Schölkopf||Causal Machine Learning||Daniel||Focusses on the connection between causal inference and machine learning. On the one hand, the authors compile a structured overview about machine learning techniques for learning causal models from data (i.e. causal discovery). On the other hand, the potential use of causal inference for machine learning (e.g. reinforcement learning or semi-supervised learning) is outlined.|
|Probabilistic Graphical Models||Daphne Koller and Nir Friedmann||Foundations||Robert||Lenghty, but thorough and easy to follow overview for probabilistc graphical models, their mathematical backgrounds and basic algorithms. Suited as an intro read, but should rather be used as a reference book. Covers the conecpts of why and how to use graphs as representatives for probability distributions.|
If you are not sure which book to read or where to find more material, check out this awesome graphic overview by Brady Neal!