Controlling Selection Bias in Causal Inference - Bareinboim, Elias; Pearl, Judea (Proceedings of the AAAI Conference on Artificial Intelligence, 2011)
Selection bias, caused by preferential exclusion of units (or samples) from the data, is a major obstacle to valid causal inferences, for it cannot be removed or even detected by randomized experiments. This paper highlights several graphical and algebraic methods capable of mitigating and sometimes eliminating this bias. These nonparametric methods generalize and improve previously reported results, and identify the type of knowledge that need to be available for reasoning in the presence of selection bias