{"title":"Madelyn Glymour","description":"\u003cp\u003eWelcome to the Madelyn Glymour collection, where the complexities of science and nature are translated into accessible and enlightening reads. Known for her keen insight and ability to demystify challenging concepts, Madelyn Glymour is an essential author for anyone intrigued by the intricacies of statistical science.\u003c\/p\u003e\n\n\u003cp\u003eThis collection includes standout works such as \u003cem\u003eCausal Inference in Statistics\u003c\/em\u003e, which is celebrated for offering a comprehensive introduction to the principles and techniques of causal analysis. Glymour's work is particularly valued for its clear explanations and practical approach, making complex theories understandable for both seasoned statisticians and newcomers alike.\u003c\/p\u003e\n\n\u003cp\u003eExplore Madelyn Glymour's books to expand your knowledge and deepen your understanding of how statistics plays a pivotal role in unraveling the intricacies of the natural world. Each book is a testament to Glymour's commitment to making science both fascinating and accessible.\u003c\/p\u003e\n\n\u003cp\u003eWhether you are a student, a professional, or a curious reader with an interest in science and nature, Madelyn Glymour's collection is sure to stimulate your mind and satisfy your curiosity.\u003c\/p\u003e","products":[{"product_id":"causal-inference-in-statistics-by-madelyn-glymour-9781119186847","title":"Causal Inference in Statistics","description":"\u003cdiv class=\"book-description\"\u003e\n\u003cp\u003e\u003cem\u003eCausal Inference in Statistics\u003c\/em\u003e\u003c\/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eA Primer\u003c\/strong\u003e\u003c\/p\u003e\n\n\u003cp\u003eCausality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as \"Does this treatment harm or help patients?\" However, despite the availability of hundreds of introductory texts on statistical methods of data analysis, until now, no beginner-level book has existed that addresses the growing array of methods for extracting causal information from data.\u003c\/p\u003e\n\n\u003cp\u003e\u003cem\u003eCausal Inference in Statistics\u003c\/em\u003e fills that gap. Through simple examples and plain language, the book explains how to define causal parameters, the assumptions required to estimate them in various situations, how to express these assumptions mathematically, whether they have testable implications, how to predict the effects of interventions, and how to reason counterfactually. These are essential tools for any student of statistics aiming to use statistical methods to answer causal questions of interest.\u003c\/p\u003e\n\n\u003cp\u003eThis book is accessible to anyone interested in interpreting data, including undergraduates, professors, researchers, and even the curious layperson. Examples span a diverse range of fields, such as medicine, public policy, and law. A brief introduction to probability and statistics is provided for newcomers, and each chapter includes study questions to reinforce the reader's understanding.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":46854214811884,"sku":"9781119186847","price":75.99,"currency_code":"NZD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0705\/7784\/8556\/files\/12815533482586.jpg?v=1759237683"}],"url":"https:\/\/bookhero.live\/collections\/madelyn-glymour.oembed","provider":"Book Hero","version":"1.0","type":"link"}