Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Guido W. Imbens 作者

Cambridge University Press

译者

2015-4-6 出版日期

644 页数

GBP 40.99 价格

Hardcover

丛书系列

9780521885881 图书编码

Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书图书标签: 计量经济学统计 Statistics Econometrics 科学研究方法论 Methodology 经济理论

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发表于2024-05-29

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Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书用户评价

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Potential outcomes approach to causality

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偏啰嗦

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勉强看完了一遍，准备二刷

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基本弃了，Rubin 体系的一家言，还这么长，还这么难懂。有其他评论说“Rubin有一种把简单事情将复杂的超能力”我看是对的。我看到过好几篇在 Rubin 体系工作的论文都是一脸懵逼，怕是被原始文献带坏了吧

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这本书让我觉得我之前统计学的东西都白学了。

Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书著者简介

Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书图书目录

Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Causal Inference for Statistics, Social, and Biomedical Sciences 在线电子书图书描述

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

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