TY  - BOOK
AU  - Molak, Aleksander
AU  - Jaokar, Ajit
TI  - Causal inference and discovery in Python: unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
SN  - 9781804612989
U1  - 005.133 
PY  - 2023///
CY  - Birmingham
PB  - Packt Publishing Ltd.
KW  - Computer science
KW  - Computer programming
KW  - Programming languages
KW  - Python
KW  - Machine Learning
N2  - Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.

(https://www.packtpub.com/product/causal-inference-and-discovery-in-python/9781804612989)
ER  -