Statistical Inference for Causal Effects, With Emphasis on Applications in Epidemiology and Medical Statistics
Statistical Inference for Causal Effects, With Emphasis on Applications in Epidemiology and Medical Statistics
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DOI：
10.1016/s0169-7161(07)27002-6
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发表时间：
2008-01-01
期刊：
影响因子：
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通讯作者：
Rubin, Donald B.
中科院分区：
文献类型：
--
作者：
Rubin, Donald B.
流行病学和医学统计学的一个中心问题是如何从随机和非随机数据中推断出治疗（即干预）的因果效应。例如，这种新药真的能减少心脏病吗？或者，与不含这种化学物质的饮用水相比，在饮用水中接触这种化学物质是否会增加癌症发病率？本章概述了基于潜在结果概念估计这种因果效应的方法。我们讨论了基于随机化的方法和贝叶斯后验预测方法。
A central problem in epidemiology and medical statistics is how to draw inferences about the causal effects of treatments (i.e., interventions) from randomized and nonrandomized data. For example, does the new drug really reduce heart disease, or does exposure to that chemical in drinking water increase cancer rates relative to drinking water without that chemical? This chapter provides an overview of the approach to the estimation of such causal effects based on the concept of potential outcomes. We discuss randomization-based approaches and the Bayesian posterior predictive approach.