Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data

Key Features

• Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more
• Discover modern causal inference techniques for average and heterogenous treatment effect estimation
• Explore and leverage traditional and modern causal discovery methods

Book Description

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.