Causal Inference in Python 1st Edition

Capture the power of cause-and-effect, not just correlation. Causal Inference in Python, 1st Edition by Matheus Facure is a practical, modern guide that puts rigorous causal methods into the hands of Python users—data scientists, analysts, researchers, and decision-makers across industries and regions.

Start solving real problems with clarity: this book explains core concepts like directed acyclic graphs (DAGs), potential outcomes, confounding, mediation, average treatment effects, and identification strategies using clear language and Python examples. Through step-by-step code demonstrations and real-world datasets, you’ll learn to move beyond predictive models to estimate causal effects that inform policy, product decisions, healthcare outcomes, and business strategy.

Imagine confidently designing experiments, adjusting for bias, and interpreting causal estimates for stakeholders in the US, UK, Europe, India, Latin America, and beyond. The author bridges theory and practice—illustrating when methods apply, common pitfalls, and how to validate findings—so readers gain both intuition and technical skill. Practical chapters focus on model selection, robustness checks, instrumental variables, and modern libraries that integrate seamlessly with typical Python workflows.

Whether you’re advancing a career in machine learning, strengthening academic research, or guiding organizational strategy, this edition equips you to ask better questions and produce defensible answers. Accessible for intermediate Python users and valuable for seasoned practitioners, it’s a go-to resource for anyone serious about causal analysis.

Order your copy of Causal Inference in Python, 1st Edition by Matheus Facure today and start turning data into decisions with confidence.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.