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Causal Data Science with Python: From Correlation to Decision
Paperback
Publisher: Independently Published
Published: Oct 12 2025
Pages: 226
Weight: 0.68
Height: 0.48 Width: 6.00 Depth: 9.00
Language: English
Key topics include counterfactual reasoning, randomized experiments, propensity score methods, instrumental variables, causal graphs (DAGs), mediation analysis, and machine learning for causal effect estimation. The text balances theory and practice, providing clear explanations of concepts such as the Rubin Causal Model, do-calculus, and Structural Causal Models (SCMs) - alongside Python implementations using libraries such as DoWhy, EconML, CausalML, and PyMC.
Whether you are a data scientist seeking to build fairer AI systems, a social scientist analyzing interventions, or a policymaker looking for evidence-based insights, this book offers the tools and reasoning framework to transform data into meaningful, actionable understanding.
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