Causal Inference for Statistics, Social, and Biomedical Sciences pdf epub mobi txt 電子書 下載2025
- 計量經濟學
- 統計
- Statistics
- Econometrics
- 科學研究
- 方法論
- Methodology
- 經濟理論
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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用戶評價
基本棄瞭，Rubin 體係的一傢言，還這麼長，還這麼難懂。有其他評論說“Rubin有一種把簡單事情將復雜的超能力”我看是對的。我看到過好幾篇在 Rubin 體係工作的論文都是一臉懵逼，怕是被原始文獻帶壞瞭吧
评分Rubin有一種把簡單事情將復雜的超能力
评分因果推斷入門
评分過長，棄
评分Causal inference beyond Regressions. But still based on the Potential Outcome Framework.
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