MARC details
| 000 -LEADER |
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| fixed length control field |
02461 a2200241 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20181011113618.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
181011b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119186847 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
IIMV |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.54 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Pearl, Judea |
| Relator term |
author |
| 9 (RLIN) |
394 |
| 245 0# - TITLE STATEMENT |
|---|
| Title |
Causal inference in statistics: a primer/ |
| Statement of responsibility, etc. |
by Judea Pearl, Madelyn Glymour, Nicholas Jewell |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Name of publisher, distributor, etc. |
John Wiley & Sons Ltd. |
| Date of publication, distribution, etc. |
2016 |
| Place of publication, distribution, etc. |
Chichester, West Sussex, UK : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 136 pages: |
| Other physical details |
Illustrations; |
| Dimensions |
27cm. |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Causal Inference in Statistics: A Primer Judea Pearl, Computer Science and Statistics, University of California Los Angeles, USA Madelyn Glymour, Philosophy, Carnegie Mellon University, Pittsburgh, USA and Nicholas P. Jewell, Biostatistics, University of California, Berkeley, USA Causality 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, "Dus this treatment harm or help patients'" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Mathematical statistics. |
| 9 (RLIN) |
395 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Causation. |
| 9 (RLIN) |
396 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Probabilities. |
| 9 (RLIN) |
397 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Glymour, Madelyn |
| Relator term |
author |
| 9 (RLIN) |
398 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Jewell, Nicholas P. |
| Relator term |
author |
| 9 (RLIN) |
399 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
