Presented By: Interdisciplinary Seminar in Quantitative Methods (ISQM)
Interdisciplinary Seminar in Quantitative Methods (ISQM): Methods for Using Selection on Observed Variables to Address Selection on Unobserved Variables
Chris Taber, University of Wisconsin
Abstract
We develop new estimation methods for estimating causal effects based on the idea that the amount of selection on the observed explanatory variables in a model provides a guide to the amount of selection on the unobservables. We discuss two approaches, one of which involves the use of a factor model as a way to infer properties of unobserved covariates from the observed covariates. We construct an interval estimator that asymptotically covers the true value of the causal effect, and we propose related confidence regions that cover the true value with fixed probability.
We develop new estimation methods for estimating causal effects based on the idea that the amount of selection on the observed explanatory variables in a model provides a guide to the amount of selection on the unobservables. We discuss two approaches, one of which involves the use of a factor model as a way to infer properties of unobserved covariates from the observed covariates. We construct an interval estimator that asymptotically covers the true value of the causal effect, and we propose related confidence regions that cover the true value with fixed probability.
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