Causal Inference for Statistics, Social, and Biomedical Sciences pdf epub mobi txt 电子书 下载 2025
- 计量经济学
- 统计
- Statistics
- Econometrics
- 科学研究
- 方法论
- Methodology
- 经济理论
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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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
评分这本书让我觉得我之前统计学的东西都白学了。
评分Causal inference beyond Regressions. But still based on the Potential Outcome Framework.
评分偏啰嗦
评分过长，弃
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