Causal discovery from data in the presence of selection bias
Cooper GF. Causal discovery from data in the presence of selection bias. In: Proceedings of the Workshop on Artificial Intelligence and Statistics (1995) 140–150.
Recent research advances have made it possible to consider using observational data to infer casual relationships among measured variables. Selection bias results from the observation of entities that are not representative of the entities that are generated by a casual process of interest. This paper shows that we can sometimes detect the presences of selection bias in observational data. The paper also demonstrates how selectin bias can hinder the discovery of casual relationships from observational data. As we will describe, the use of experimental data (e.g., data from randomized, controlled trials) to discover casual relationships con be susceptible as well to problems involving selection bias. We offer suggestions for how to proceed with casual discovery in the face of selection bias.
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