Causal Inference for Healthcare & Biology with Python: From Observational Data to Treatment Effects in Clinical and Biological Systems (Python for Health Science and Bioinformatics Book 6)

Description

Reactive Publishing Causal Inference for Healthcare & Biology with Python is a practical, systems-level guide to answering the hardest question in clinical and biological data analysis: what actually causes what . In healthcare and biology, randomized controlled trials are often expensive, slow, unethical, or impossible. Most real-world decisions must be made from observational data, EHRs, registries, cohort studies, omics data, and longitudinal experiments. This book shows how to move beyond correlation and predictive modeling to estimate true causal effects using modern causal inference frameworks implemented in Python. Written for practitioners, researchers, and applied data scientists, this book bridges statistics, epidemiology, and machine learning, translating theory into reproducible workflows you can apply to real clinical and biological systems. What you’ll learn: How to formalize causal questions using counterfactual reasoning and causal graphs (DAGs) When and how observational data can be used to estimate treatment effects Confounding, selection bias, collider bias, and how to detect and correct them Propensity score methods: matching, weighting, stratification Regression adjustment and doubly robust estimators Instrumental variables and natural experiments in healthcare settings Causal inference with time-varying treatments and longitudinal data Difference-in-differences and causal panel methods Causal discovery and structure learning in biological systems Translating causal estimates into clinical, biological, and policy decisions Practical, Python-first approach: Hands-on examples using real healthcare and biological data structures Implementations with Python libraries such as NumPy, pandas, statsmodels, and causal inference toolkits Clear guidance on assumptions, diagnostics, and failure modes Emphasis on interpretability and decision relevance, not black-box prediction Who this book is for: Healthcare data scientists and clinical researchers Epidemiologists and biostatisticians working with observational data Computational biologists and bioinformaticians Machine-learning practitioners transitioning from prediction to causation Graduate students and professionals working in applied health analytics This is not a theoretical math text, nor a shallow overview. Causal Inference for Healthcare & Biology with Python is a rigorous, applied guide for professionals who need defensible answers from imperfect data, where decisions affect patients, treatments, and biological understanding. If your work depends on turning complex observational data into reliable treatment insights, this book provides the conceptual clarity and practical tools to do it correctly.