Identifying Selection Bias from Observational Data.

Access to a representative sample from the population is an assumption that underpins all of machine learning. Unfortunately, selection effects can cause observations to instead come from a subpopulation, by which our inferences may be subject to bias. It is therefore essential to know whether or not a sample is affected by selection effects. We study under which conditions we can identify selection bias and give results for both parametric and non-parametric families of distributions. Based on these results, we develop two practical methods to determine whether or not an observed sample comes from a distribution subject to selection bias. Through extensive evaluation on synthetic and real-world data, we verify that our methods beat the state of the art both in detecting as well as characterizing selection bias.