Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in ei-ther experimental or observational studies. In this paper, we provide complete graphical and algorithmic conditions for recovering conditional probabilities from selection biased data. We also provide graphical conditions for recoverabil-ity when unbiased data is available over a subset of the vari-ables. Finally, we provide a graphical condition that gener-alizes the backdoor criterion and serves to recover causal ef-fects when the data is collected under preferential selection.
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Recovering from Selection Bias in Causal and Statistical Inference
OpenAlex · Bayesian Modeling and Causal Inference · 2022
Abstract
Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in either experimental or observational studies. In this paper, we provide complete graphical and algorithmic conditions for recovering conditional probabilities from selection biased data. We also provide graphical conditions for recoverability when unbiased data is available over a subset of the variables. Finally, we provide a graphical condition that generalizes the backdoor criterion and serves to recover causal effects when the data is collected under preferential selection.