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Title: Extremal quantile treatment effects for heavy-tailed distributions Authors:  Sebastian Engelke - University of Geneva (Switzerland) [presenting]
David Deuber - ETHZ (Switzerland)
Marloes Maathuis - ETH Zurich (Switzerland)
Jinzhou Li - ETHZ (Switzerland)
Abstract: Causal inference for rare events has important applications in many fields such as medicine, climate science and finance. We introduce an extremal quantile treatment effect as the difference of extreme quantiles of the potential outcome distributions. Estimation of this effect is based on extrapolation results from extreme value theory in combination with a new counterfactual Hill estimator that uses propensity scores as adjustment. We establish the asymptotic theory of this estimator and propose a variance estimation procedure that allows for valid statistical inference. Our method is applied to analyze the effect of college education on high wages.