Controlling Selection Bias in Causal Inference

Selection bias, caused by preferential exclusion of samples from the data, is a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can hardly be detected in either experimental or observational studies. This paper highlights several algebraic and graphical methods capable of mitigating and sometimes eliminating this bias. These nonparametric methods generalize previously reported results, and identify the type of knowledge that need to be available for reasoning in the presence of selection bias.

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: Elias Bareinboim, Judea Pearl
• Keywords: AAAI 2011, Graphical Methods, Intelligent Agents, Selection Bias, Statistical Inferences