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.
Year: 2014

URL: https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8628/8707
License: No license specified, the work may be protected by copyright.

@paper{AAAI148628,
	author = {Elias Bareinboim and Jin Tian and Judea Pearl},
	title = {Recovering from Selection Bias in Causal and Statistical Inference},
	conference = {AAAI Conference on Artificial Intelligence},
	year = {2014},
	keywords = {selection bias; sampling bias; causal inference; causality; statistical inference},
	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.},

	url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8628/8707}
}