{"product_id":"causal-data-science-with-python-from-correlation-to-decision-9798269548258","title":"Causal Data Science with Python: From Correlation to Decision","description":"\u003cp\u003e • Author(s): Dorcas O. Folarin | Mary M. Adepoju | Joseph Solomon\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003eIn the age of big data, correlation is everywhere - but causation is what truly drives understanding and decision-making. \u003cb\u003e\u003ci\u003eCausal Data Science with Python: From Correlation to Decision\u003c\/i\u003e\u003c\/b\u003e bridges the gap between predictive modeling and causal reasoning, offering a practical, hands-on guide to uncovering cause-and-effect relationships in data.\u003cbr\u003eThis book introduces the principles of \u003cb\u003ecausal inference\u003c\/b\u003e and their implementation in \u003cb\u003ePython\u003c\/b\u003e, combining the rigor of statistics with the flexibility of modern machine learning. Through real-world examples and step-by-step coding exercises, readers learn to move beyond simple associations and make robust causal claims that support confident decisions in business, healthcare, economics, and the social sciences.\u003cbr\u003eKey topics include \u003cb\u003ecounterfactual reasoning\u003c\/b\u003e, \u003cb\u003erandomized experiments\u003c\/b\u003e, \u003cb\u003epropensity score methods\u003c\/b\u003e, \u003cb\u003einstrumental variables\u003c\/b\u003e, \u003cb\u003ecausal graphs (DAGs)\u003c\/b\u003e, \u003cb\u003emediation analysis\u003c\/b\u003e, and \u003cb\u003emachine learning for causal effect estimation\u003c\/b\u003e. The text balances theory and practice, providing clear explanations of concepts such as the \u003cb\u003eRubin Causal Model\u003c\/b\u003e, \u003cb\u003edo-calculus\u003c\/b\u003e, and \u003cb\u003eStructural Causal Models (SCMs)\u003c\/b\u003e - alongside Python implementations using libraries such as DoWhy, EconML, CausalML, and PyMC.\u003cbr\u003eWhether 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.\u003c\/p\u003e","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46332058960023,"sku":"9798269548258","price":1784.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798269548258.webp?v=1768725079","url":"https:\/\/atlanticbooks.com\/products\/causal-data-science-with-python-from-correlation-to-decision-9798269548258","provider":"Atlantic Books","version":"1.0","type":"link"}