{"product_id":"138234","title":"Practical Causal Inference with Python ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPCfNU1Sh39xrS9YMJOqUtkGFDRZ.png?v=1765061432\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Practical Causal Inference with Python \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/125196916\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eCausal inference for data-driven, insightful decision-making,\u003c\/b\u003e \u003cbr\u003eSuccessful business policy decisions through effective impact analysis\u003cbr\u003e\u003cbr\u003e How much more buyers will there be if we increase our online marketing budget by $1? How do we identify customers who only buy when they receive a discount coupon? How can we develop an optimal pricing strategy? Causal inference is the most useful way to understand how the factors you manage affect your desired business metrics.\u003cbr\u003e Causal inference can be easily implemented with just a few lines of Python code.\u003cbr\u003e\u003cbr\u003e This book demonstrates the untapped potential of causal inference in estimating influence and effects. \u003cbr\u003eThis book covers classic causal inference methods, such as A\/B testing, linear regression, propensity scores, control group synthesis, and double differencing, for managers, data scientists, and data analysts, as well as modern approaches, such as machine learning for heterogeneous effect estimation, with practical application examples.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e[PART 1: Basic Causal Inference]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 1: Introduction to Causal Inference\u003cbr\u003e _1.1 The concept of causal inference\u003cbr\u003e _1.2 Purpose of causal inference\u003cbr\u003e _1.3 Machine Learning and Causal Inference\u003cbr\u003e _1.4 Correlation and Causality\u003cbr\u003e _1.5 bias\u003cbr\u003e _1.6 Identifying Causal Effects\u003cbr\u003e _1.7 Summary\u003cbr\u003e\u003cbr\u003e Chapter 2: Randomized Experiments and Basic Statistics Review\u003cbr\u003e _2.1 Ensuring independence through random assignment\u003cbr\u003e _2.2 A\/B Testing Case\u003cbr\u003e _2.3 Ideal Experiment\u003cbr\u003e _2.4 The most dangerous formula\u003cbr\u003e _2.5 Standard error of the estimate\u003cbr\u003e _2.6 Confidence interval\u003cbr\u003e _2.7 Hypothesis Testing\u003cbr\u003e _2.8 p value\u003cbr\u003e _2.9 Black power\u003cbr\u003e _2.10 Calculating sample size\u003cbr\u003e _2.11 Summary\u003cbr\u003e\u003cbr\u003e Chapter 3 Graph Causal Model \u003cbr\u003e_3.1 Thinking about causality\u003cbr\u003e _3.2 Graph Model Intensive Training\u003cbr\u003e _3.3 Reinterpretation of Identification\u003cbr\u003e _3.4 Conditional Independence Assumption and Correction Formula\u003cbr\u003e _3.5 Positive assumption\u003cbr\u003e _3.6 Concrete Identification Examples\u003cbr\u003e _3.7 Confounding Bias\u003cbr\u003e _3.8 Selection Bias\u003cbr\u003e _3.9 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 2 BIAS CORRECTION]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 4: Useful Linear Regression\u003cbr\u003e _4.1 The Need for Linear Regression\u003cbr\u003e _4.2 Regression Analysis Theory\u003cbr\u003e _4.3 Frisch-Warr-Lobel theorem and orthogonalization\u003cbr\u003e _4.4 Regression Analysis as a Results Model\u003cbr\u003e _4.5 Positive and Extrapolation\u003cbr\u003e _4.6 Nonlinearity in Linear Regression\u003cbr\u003e _4.7 Regression analysis using dummy variables\u003cbr\u003e _4.8 Omitted variable bias\u003cbr\u003e _4.9 Neutral control variables\u003cbr\u003e _4.10 Summary\u003cbr\u003e\u003cbr\u003e Chapter 5 Propensity Score\u003cbr\u003e _5.1 Effectiveness of Manager Training\u003cbr\u003e _5.2 Regression Analysis and Calibration\u003cbr\u003e _5.3 Propensity Score\u003cbr\u003e _5.4 Design vs.\u003cbr\u003e Model-based identification\u003cbr\u003e _5.5 Double Robust Estimation\u003cbr\u003e _5.6 Generalized propensity score in continuous treatment\u003cbr\u003e _5.7 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 3: Heterogeneous Effects and Personalization]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 6: Heterogeneous Treatment Effects\u003cbr\u003e _6.1 From ATE to CATE\u003cbr\u003e _6.2 Why Predictions Are Not the Answer\u003cbr\u003e _6.3 Obtaining CATE using regression analysis  \u003cbr\u003e_6.4 Evaluating CATE Predictions\u003cbr\u003e _6.5 Effects according to model atmosphere\u003cbr\u003e _6.6 Cumulative Effect Curve\u003cbr\u003e _6.7 Cumulative gain curve\u003cbr\u003e _6.8 Target Conversion\u003cbr\u003e _6.9 When the prediction model is good for effect sorting\u003cbr\u003e _6.10 CATE for Decision Making\u003cbr\u003e _6.11 Summary\u003cbr\u003e\u003cbr\u003e Chapter 7 Meta Runner\u003cbr\u003e _7.1 Discrete Treatment Meta Runner\u003cbr\u003e _7.2 Continuous Kill Meta Runner\u003cbr\u003e _7.3 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 4 ​​Panel Data]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 8 Double Difference Method\u003cbr\u003e _8.1 Panel data\u003cbr\u003e _8.2 Standard double difference method\u003cbr\u003e _8.3 Identification Assumptions\u003cbr\u003e _8.4 Effect variation over time\u003cbr\u003e _8.5 Double Difference Method and Covariance\u003cbr\u003e _8.6 Double Robust Double Difference Method\u003cbr\u003e _8.7 Introduction of time difference in church\u003cbr\u003e _8.8 Summary\u003cbr\u003e\u003cbr\u003e Chapter 9 Control Group Synthesis\u003cbr\u003e _9.1 Online Marketing Dataset\u003cbr\u003e _9.2 Matrix Representation\u003cbr\u003e _9.3 Control group synthesis and horizontal regression analysis\u003cbr\u003e _9.4 Standard Control Group Synthesis Method\u003cbr\u003e _9.5 Control group synthesis and covariates\u003cbr\u003e _9.6 Control group synthesis and bias elimination\u003cbr\u003e _9.7 Inference\u003cbr\u003e _9.8 Synthetic double difference method\u003cbr\u003e _9.9 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 5 ALTERNATIVE EXPERIMENTAL DESIGN]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Chapter 10: Local and Switchback Experiments\u003cbr\u003e _10.1 Regional Experiment \u003cbr\u003e_10.2 Control group synthesis design\u003cbr\u003e _10.3 Switchback Experiment\u003cbr\u003e _10.4 Summary\u003cbr\u003e\u003cbr\u003e Chapter 11 Non-response and Instrumental Variables\u003cbr\u003e _11.1 Non-response\u003cbr\u003e _11.2 Expanding Potential Outcomes\u003cbr\u003e _11.3 Instrumental Variable Identification Assumptions\u003cbr\u003e _11.4 Step 1\u003cbr\u003e _11.5 Step 2\u003cbr\u003e _11.6 Two-stage least squares method\u003cbr\u003e _11.7 Standard error\u003cbr\u003e _11.8 Adding control variables and instrumental variables\u003cbr\u003e _11.9 Discontinuous Design\u003cbr\u003e _11.10 Summary\u003cbr\u003e\u003cbr\u003e Chapter 12: What More to Learn\u003cbr\u003e _12.1 Discovering causal relationships\u003cbr\u003e _12.2 Sequential Decision Making\u003cbr\u003e _12.3 Causal Reinforcement Learning\u003cbr\u003e _12.4 Causal Prediction\u003cbr\u003e _12.5 Domain Adaptation\u003cbr\u003e _12.6 Summary\u003cbr\u003e\u003cbr\u003e Epilogue: Applying Causal Inference to Practice\u003cbr\u003e Search\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4439\/MidCate005\/443849221.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eLearning Causal Inference with Python:\u003cbr\u003e Understand business principles and develop successful strategies.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e “We live our daily lives unconsciously making countless causal inferences.\u003cbr\u003e Although causal inference is so closely related to our way of thinking, it is easy to overlook its importance and difficulty. \u003cbr\u003eBut as data analytics begins to replace our reasoning and decision-making, the causal relationships between phenomena are becoming increasingly obscure, buried in the deluge of big data.\u003cbr\u003e Inferring cause and effect from data is much more difficult than you might think.\u003cbr\u003e That doesn't mean it's impossible.\u003cbr\u003e Over the past several years, interest in causal inference has grown day by day, and the need for causal inference has emerged not only in academia but also among developers and data analysts in the field. This has brought about a change in the data science trend that has been focused on building big data and developing AI\/ML models (with the primary purpose of prediction).\u003cbr\u003e In a world where Korean language learning materials are scarce, this book will be a welcome relief for those seeking to delve into causal inference.\u003cbr\u003e \u003cbr\u003eThis book explains the core concepts of causal inference in an accessible way without being overly focused on the mathematics and theory of statistics and machine learning, and even faithfully incorporates the latest research findings.\u003cbr\u003e Moreover, since it provides a balanced approach to practical and hands-on learning through Python practice, it serves as a valuable guide and reference book that you can keep by your side and refer to when needed.\u003cbr\u003e I highly recommend this book to researchers new to causal inference and to practicing data analysts.”\u003cbr\u003e - From the introduction, ‘The Editor’s Note’\u003cbr\u003e\u003cbr\u003e \u003cb\u003eKey Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e · Learn the basic concepts and applications of causal inference.\u003cbr\u003e Understanding the relationship between causal inference and bias\u003cbr\u003e Solving business problems using causal inference\u003cbr\u003e · Observe customers over time using causal inference\u003cbr\u003e · Learn why causal effects may vary across experimental subjects. \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e March 5, 2024\u003c\/div\u003e\n\n \u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e- \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 464 pages | 832g | 183*235*20mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791169212113\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1169212115 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893219459114,"sku":"138234","price":47.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/902a1afbd978e4f69105bab6270e50c1.jpg?v=1765392333","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138234","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}