We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …

Knowledge of causal effects is of great importance to decision makers in a wide variety of
settings. In many cases, however, these causal effects are not known to the decision makers …

This article proposes doubly robust estimators for the average treatment effect on the treated
(ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID …

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical
applications, such as personalized medicine and optimal resource allocation. In this article …

I believe that machine learning (ML) will have a dramatic impact on the field of economics
within a short time frame. Indeed, the impact of ML on economics is already well underway …

There is growing interest in estimating and analyzing heterogeneous treatment effects in
experimental and observational studies. We describe a number of metaalgorithms that can …

We revisit the classic semi‐parametric problem of inference on a low‐dimensional
parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from …

The envelope spike (S) proteins of MERS-CoV and SARS-CoV determine the virus host
tropism and entry into host cells, and constitute a promising target for the development of …

We provide new statistical guarantees for transfer learning via representation learning--
when transfer is achieved by learning a feature representation shared across different tasks …

Information geometry is the differential geometric treatment of statistical models. It thereby
provides the mathematical foundation of statistics. Information geometry therefore is of …