{"title":"Nicholas P. Jewell","description":"\u003cp\u003eNicholas P. Jewell is a renowned statistician and author whose works have made significant contributions to the field of statistical science. With a career that bridges both academic and applied statistics, Jewell has become a pivotal figure in understanding complex data.\u003c\/p\u003e\n\n\u003cp\u003eHis book, \u003cem\u003eCausal Inference in Statistics\u003c\/em\u003e, is a cornerstone in the Science \u0026amp; Nature category, guiding readers through the intricacies of determining causality within data sets. This work reflects Jewell's commitment to making sophisticated statistical concepts accessible to a broader audience, while maintaining academic rigour.\u003c\/p\u003e\n\n\u003cp\u003eJewell’s writing resonates with both beginners and seasoned professionals, demystifying the art of statistical analysis with clarity and precision. His approachable style and comprehensive knowledge make his books an invaluable resource for anyone keen on exploring the nuances of statistics in the realms of science and nature.\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.pro\/collections\/nicholas-p-jewell.oembed","provider":"Book Hero","version":"1.0","type":"link"}