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.
Recovering from Selection Bias in Causal and Statistical Inference مقاله هوش مصنوعی 2014