Causal Inference

A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy.

Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce?

Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.

一本非技術性指南，介紹現代因果推論的基本概念，並以健康、經濟和公共政策為例進行說明。

兩種抗病毒藥物中，哪一種對感染埃博拉病毒的人救助最多？每天喝一杯酒是否會延長或縮短壽命？中獎彩票是否會增加或減少破產的可能性？如何辨識引起疾病的基因？工會是否會提高工資？某些抗生素是否有致命副作用？所得稅抵免是否有助於人們就業？《因果推論》簡要且非技術性地介紹了隨機實驗、傾向分數、自然實驗、工具變數、敏感性分析和準實驗設備等概念。這些概念通過醫學、流行病學、經濟學和商業、社會科學以及公共政策的例子進行了說明。

Paul R. Rosenbaum is the Robert G. Putzel Professor Emeritus of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He is the author of Observation and Experiment: An Introduction to Causal Inference, Design of Observational Studies, Observational Studies, and Replication and Evidence Factors in Observational Studies.

Paul R. Rosenbaum是賓夕法尼亞大學沃頓商學院統計學和數據科學的羅伯特·G·普茲爾名譽教授。他是《觀察和實驗：因果推論入門》、《觀察研究設計》、《觀察研究》和《觀察研究中的複製和證據因素》的作者。