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社会科学中的因果推断与异质性偏差

CAUSAL INFERENCE AND HETEROGENEITY BIAS IN SOCIAL SCIENCE.

作者信息

Xie Yu

机构信息

University of Michigan.

出版信息

Inf Knowl Syst Manage. 2011 Jan 1;10(1):279-289. doi: 10.3233/IKS-2012-0197.

DOI:10.3233/IKS-2012-0197
PMID:23970824
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3747843/
Abstract

Because of population heterogeneity, causal inference with observational data in social science may suffer from two possible sources of bias: (1) bias in unobserved pretreatment factors affecting the outcome even without treatment; and (2)bias due to heterogeneity in treatment effects. Even when we control for observed covariates, these two biases may occur if the classic ignorability assumption is untrue. In cases where the ignorability assumption is true, "composition bias" can occur if treatment propensity is systematically associated with heterogeneous treatment effects.

摘要

由于人口异质性，社会科学中利用观测数据进行因果推断可能会受到两种潜在偏差来源的影响：（1）即使在未接受治疗的情况下，未观察到的预处理因素对结果产生的偏差；（2）治疗效果异质性导致的偏差。即使我们控制了观测到的协变量，如果经典的可忽略性假设不成立，这两种偏差仍可能出现。在可忽略性假设成立的情况下，如果治疗倾向与异质的治疗效果存在系统性关联，就可能出现“构成偏差”。