Econometrics (econ.EM)

We introduce a new convexified matching method for missing value imputation and individualized inference inspired by computational optimal transport.

Detecting weak, systematic distribution shifts and quantitatively modeling individual, heterogeneous responses to policies or incentives have found increasing empirical applications in social and economic sciences. We propose a model for weak distribution shifts via displacement interpolation, drawing from the optimal transport theory.

This paper proposes a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification.

Can deep neural networks with standard archtectures estimate treatment effects and perform downstream uncertainty quantification tasks?