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Download the fantastic book titled Causal Inference written by Miquel A. Hernan, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Causal Inference", which was released on 07 July 2019. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Medical genre.

Summary of Causal Inference by Miquel A. Hernan PDF

The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Causal Inference

Detail About Causal Inference PDF

  • Author : Miquel A. Hernan
  • Publisher : CRC Press
  • Genre : Medical
  • Total Pages : 352 pages
  • ISBN : 9781420076165
  • PDF File Size : 20,7 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Causal Inference

Causal Inference
  • Publisher : CRC Press
  • File Size : 50,7 Mb
  • Release Date : 07 July 2019
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The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible

Causal Inference

Causal Inference
  • Publisher : Yale University Press
  • File Size : 36,7 Mb
  • Release Date : 26 January 2021
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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

Fundamentals of Causal Inference

Fundamentals of Causal Inference
  • Publisher : CRC Press
  • File Size : 30,9 Mb
  • Release Date : 10 November 2021
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One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal

Elements of Causal Inference

Elements of Causal Inference
  • Publisher : MIT Press
  • File Size : 22,9 Mb
  • Release Date : 29 November 2017
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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

Causal Inference in Statistics

Causal Inference in Statistics
  • Publisher : John Wiley & Sons
  • File Size : 31,7 Mb
  • Release Date : 25 January 2016
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CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions

Causality

Causality
  • Publisher : Cambridge University Press
  • File Size : 51,6 Mb
  • Release Date : 14 September 2009
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Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural

Explanation in Causal Inference

Explanation in Causal Inference
  • Publisher : Oxford University Press, USA
  • File Size : 23,9 Mb
  • Release Date : 01 May 2024
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The book begins with a comprehensive introduction to mediation analysis, including chapters on concepts for mediation, regression-based methods, sensitivity analysis, time-to-event outcomes, methods for multiple mediators, methods for time-varying mediation

An Introduction to Causal Inference

An Introduction to Causal Inference
  • Publisher : Createspace Independent Publishing Platform
  • File Size : 20,9 Mb
  • Release Date : 01 May 2024
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This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special