| CRC Press

83,340원  | 20250523  | 9780367711337

Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. The text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Rosenbaum, Paul R.  | MIT Press

31,130원  | 20230307  | 9780262545198

Vera Sheridan provides the first complex and nuanced look into the daily routines, state policies, and international motives that shaped life for Hungarian refugees living in Ireland's first refugee camp.

 | KS OmniScriptum Publishing

180,250원  | 20140219  | 9783659520594

Often researchers using non-quasi-experimental (NQE) study designs face situations where they must conduct comparative studies between two or more programs or policies to determine outcome effects for informed policy decisions. Randomized design is the strongest approach, but often in social science and educational studies, subject matching becomes an alternative study design.

 |

138,910원  | 20240731  | 9781032758626

This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference.

 | O'Reilly Media

124,610원  | 20240102  | 9781098140250

In this book, author Matheus Facure explains the untapped potential of causal inference for estimating impacts and effects.

 | Manning Publications

86,080원  | 20230725  | 9781633439658

Causal Inference for Data Science introduces data-centric techniques and methodologies you can use to estimate causal effects. The numerous insightful examples show you how to put causal inference into practice in the real world.

Li, Sheng, Chu, Zhixuan  | Springer

283,480원  | 20231210  | 9783031350504

Cunningham, Scott  | Yale University Press

55,120원  | 20210126  | 9780300251685

An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

Babette A. Brumback  | CRC Press

134,640원  | 20211110  | 9780367705053

Explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models, including standardization, difference-in-differences estimation, the front-door method, instrumental variables estimation, and propensity score methods.

Momiao Xiong  | CRC Press

96,160원  | 20240527  | 9781032193281

Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination.

Molak, Aleksander  | Packt Publishing

46,000원  | 20230801  | 9781804612989

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book Description Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.

Peters, Jonas, Dominik Janzing, Bernhard Scholkopf  | Mit Press

55,000원  | 20171129  | 9780262037310

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Paul Rosenbaum  | Harvard Univ Pr

39,290원  | 20190715  | 9780674241633

The story of tobacco's fortunes seems simple: science triumphed over addiction and profit. Yet the reality is more complicated-and more political. Historically it was not just bad habits but also the state that lifted the tobacco industry. What brought about change was not medical advice but organized pressure: a movement for nonsmoker's rights.

 | KS OmniScriptum Publishing

113,750원  | 20100409  | 9783838335391

Causal inference is an important but controversial topic in the social sciences in that it is difficult to statistically control for all possible confounding variables. To address this concern, this monograph introduces a reference distribution of the confounding that is the product of two dependent correlation coefficients and illustrates how to use the reference distribution to investigate the robustness of a cause inference to the impact of a confounding variable.

 | Taylor & Francis Ltd

245,800원  | 20220308  | 9780367859404

Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination.