In this paper, noting that the prediction of time series follows the temporal order of data, we
propose a frequentist model averaging method based on forward-validation. Our method …

Uncovering causal effects at various levels of granularity provides substantial value to
decision makers. Comprehensive machine learning approaches to causal effect estimation …

This article develops an empirical balancing approach for the estimation of treatment effects
under two-sided noncompliance using a binary instrumental variable. The method weighs …

To address the bias of the canonical two-way fixed effects estimator for difference-in-
differences under staggered adoptions, Wooldridge (2021) proposed the extended two-way …

The fixed effects (FE) panel model is one of the main econometric tools in empirical
economic research. A major practical limitation is that the parameters on time-constant …

This dissertation presents three novel contributions to the fields of machine learning and
causal inference. The unifying theme is leveraging sparsity to improve the interpretability …

This thesis consists of three self-contained chapters. The first chapter, which is joint work
with Enno Mammen and Ralf A. Wilke, proposes and implements a new estimation …