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In the (frequently-occurring) context of estimating the impact of a "treatment" (an intervention, or experiment) on an outcome of interest, using only cross-sectional data, selection biases are a common problem. Selection biases occur when the treatment is not randomly distributed, but rather, "treated" individuals (even before treatment) differ from "untreated" ones in unobserved ways that are relevant to the outcome. If such a bias is not properly accounted for, conventional methods will, in turn, yield biased estimates of the treatment effect. This talk explains the selection bias phenomenon in intuitive and partly technical terms, sketches a technical solution to it (the endogenous switching regression approach), and presents an empirical application to estimating the effect of teleworking on vehicle-miles traveled. Speaker - Patricia L. Mokhtarian, Clifford & William Greene, Jr. Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology