We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24...

Agents operating in illegal markets cannot resort to the justice system to guarantee property rights, to enforce contracts, or to seek protection from competitors' improper behaviors. In these contexts, violence is used to enforce previous agreements and to fight for market share. This...

We present results from a field experiment testing the gift-exchange hypothesis inside a tree-planting firm paying its workforce incentive contracts. Firm managers told a crew of tree planters they would receive a pay raise for one day as a result of a surplus not attributable to past planting...

We investigate the economic relevance and the composition of gifts within a firm where output is contractible. We develop a structural econometric model that identifies workers' optimal reaction to monetary gifts received from their employer. We estimate the model using data from two separate...

We develop the case of two-stage least squares estimation (2SLS) in the general framework of Athey et al. (Generalized Random Forests, Annals of Statistics, Vol. 47, 2019) and provide a software implementation for R and C++. We use the method to revisit the classic application of instrumental...

We develop the case of two-stage least squares estimation (2SLS) in the general framework of Athey et al. (Generalized Random Forests, Annals of Statistics, Vol. 47, 2019) and provide a software implementation for R and C++. We use the method to revisit the classic application of instrumental...

In this paper we propose the use of machine learning methods to estimate inequality of opportunity. We illustrate how our proposed methods - conditional inference regression trees and forests - represent a substantial improvement over existing estimation approaches. First, they reduce the risk...

This study employs six Machine Learning methods - Logit, Lasso-Logit, Ridge-Logit, Random Forest, Extreme Gradient Boosting, and an Ensemble - alongside registry data on abortions in Spain from 2011-2019 to predict multiple abortions and assess monetary savings through targeted interventions. We...

Many of our most pressing challenges, from combating climate change to dealing with pandemics, are collective action problems: situations in which individual and collective interests conflict with each other. In such situations people face a dilemma about making individually costly but...