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 …

In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

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 …

This paper addresses the use of neural networks for the estimation of treatment effects from
observational data. Generally, estimation proceeds in two stages. First, we fit models for the …

In this survey we discuss the recent causal panel data literature. This recent literature has
focused on credibly estimating causal effects of binary interventions in settings with …

Innovations in statistical technology in functions including credit‐screening have raised
concerns about distributional impacts across categories such as race. Theoretically …

Abstract Evaluation of treatment effects and more general estimands is typically achieved via
parametric modeling, which is unsatisfactory since model misspecification is likely. Data …

In addition to efficient statistical estimators of a treatment's effect, successful application of
causal inference requires specifying assumptions about the mechanisms underlying …

Machine learning is a field at the intersection of statistics and computer science that uses
algorithms to extract information and knowledge from data. Its applications increasingly find …

This paper considers generalized least squares (GLS) estimation for linear panel data
models. By estimating the large error covariance matrix consistently, the proposed feasible …