Posted by **arundhati** at Feb. 5, 2016

2014 | ISBN-10: 1107694167, 1107065070 | 524 pages | PDF | 8 MB

Posted by **Nice_smile)** at Sept. 24, 2015

English | Sep. 2, 2002 | ISBN: 0521529212 | 332 Pages | PDF | 4.56 MB

Bill Shipley explores the logical and methodological relationships between correlation and causation.

Posted by **BUGSY** at Sept. 12, 2015

English | Nov. 30, 2009 | ISBN: 0521195004, 0521123909 | 417 Pages | PDF | 1 MB

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology.

Posted by **ChrisRedfield** at July 18, 2015

Published: 2012-10-30 | ISBN: 0230240704 | PDF | 272 pages | 2.53 MB

Posted by **BUGSY** at May 18, 2015

English | Sep 3, 2004 | ISBN: 047009043X | 411 Pages | PDF | 5 MB

This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts.

Posted by **nebulae** at March 21, 2015

English | ISBN: 1446252442 | 2014 | 424 pages | PDF | 7 MB

Posted by **elodar** at Sept. 14, 2013

English | 2000-11-20 | ISBN: 0521791537 | 330 pages | PDF | 4.56 mb

Posted by **arundhati** at April 1, 2018

2017 | ISBN-10: 0262037319 | 288 pages | PDF | 21 MB

Posted by **AvaxGenius** at March 28, 2018

English | PDF,EPUB | 2018 | 655 Pages | ISBN : 3319653032 | 17.35 MB

This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference.

Posted by **AvaxGenius** at Feb. 25, 2018

This book examines how legal causation inference and epidemiological causal inference can be harmonized within the realm of jurisprudence, exploring why legal causation and epidemiological causation differ from each other and defining related problems. The book also discusses how legal justice can be realized and how victims’ rights can be protected. It looks at epidemiological evidence pertaining to causal relationships in cases such as smoking and the development of lung cancer, and enables readers to correctly interpret and rationally use the results of epidemiological studies in lawsuits.