Causal Effect Identification by Adjustment under Confounding and Selection Biases
Causal Effect Identification by Adjustment under Confounding and Selection Biases
复制标题
通过混杂和选择偏差下的调整来识别因果效应
DOI：
10.1609/aaai.v31i1.11060
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发表时间：
2017
影响因子：
5.8
通讯作者：
E. Bareinboim
中科院分区：
文献类型：
--
作者：
Juan David Correa;E. Bareinboim
控制选择和混淆偏见是经验科学和人工智能任务中最具挑战性的两个问题。协变量调整(或后门调整)是用于控制混合偏差的最普遍的技术，但同样忽略了样本选择的问题。在这篇文章中，我们介绍了一种广义协变量调整，它同时控制混杂偏差和选择偏差。我们首先从优先选择下收集的观测分布出发，推导出用协变量平差恢复因果效应的充要条件。然后，我们放宽这一设置，以考虑对一组协变量(例如，从人口普查数据获得的年龄和性别分布)进行额外的、无偏的测量可供使用的情况。最后，我们给出了一个多项式延迟的完整算法，当混杂和选择偏差同时存在，且无偏数据可用时，找到所有可允许调整的协变量集。
Controlling for selection and confounding biases are two of the most challenging problems in the empirical sciences as well as in artificial intelligence tasks. Covariate adjustment (or, Backdoor Adjustment) is the most pervasive technique used for controlling confounding bias, but the same is oblivious to issues of sampling selection. In this paper, we introduce a generalized version of covariate adjustment that simultaneously controls for both confounding and selection biases. We first derive a sufficient and necessary condition for recovering causal effects using covariate adjustment from an observational distribution collected under preferential selection. We then relax this setting to consider cases when additional, unbiased measurements over a set of covariates are available for use (e.g., the age and gender distribution obtained from census data). Finally, we present a complete algorithm with polynomial delay to find all sets of admissible covariates for adjustment when confounding and selection biases are simultaneously present and unbiased data is available.