Many empirical questions in economics and other social sciences depend on causal effects
of programs or policies. In the last two decades, much research has been done on the …

Originating in econometrics and statistics, the counterfactual model provides a natural
framework for clarifying the requirements for valid causal inference in the social sciences …

Employees around the world have experienced sudden, significant changes in their work
and family roles due to the COVID-19 pandemic. However, applied psychologists have …

This paper combines pre-pandemic face-to-face survey data with follow up phone surveys
collected in April-May 2020 to examine the implication of the COVID-19 pandemic on …

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 …

In this article, we consider identification, estimation, and inference procedures for treatment
effect parameters using Difference-in-Differences (DiD) with (i) multiple time periods,(ii) …

Abstract Machine learning with missing data has been approached in many different ways,
including feature imputation where missing feature values are estimated based on observed …

This paper examines the impacts of access to extension services and cooperative
membership on technology adoption, asset ownership and poverty using household-level …

The global coronavirus pandemic (COVID-19) dramatically slowed economic activity as
governments implemented lockdown measures, individuals reacted by reducing both their …

Most data for evaluating and training recommender systems is subject to selection biases,
either through self-selection by the users or through the actions of the recommendation …