Search Results for author:
Found 10 papers, 2 papers with code
Teamwork and Spillover Effects in Performance Evaluations
Coworker shooting performance, meaningfully impacts both, manager decisions and third-party expert evaluations of individual performance.
Causal Machine Learning for Moderation Effects
Adding additional identifying assumptions allows specific balanced differences in treatment effects between groups to be interpreted causally, leading to the causal balanced group average treatment effect.
The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates
This paper investigates the finite sample performance of a range of parametric, semi-parametric, and non-parametric instrumental variable estimators when controlling for a fixed set of covariates to evaluate the local average treatment effect.
Modified Causal Forest
Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers.
Active labour market policies for the long-term unemployed: New evidence from causal machine learning
Active labor market programs are important instruments used by European employment agencies to help the unemployed find work.
The Effect of Sport in Online Dating: Evidence from Causal Machine Learning
We find that for male users, doing sport on a weekly basis increases the probability to receive a first message from a woman by 50%, relatively to not doing sport at all.
Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator.
Random Forest Estimation of the Ordered Choice Model
In this paper we develop a new machine learning estimator for ordered choice models based on the random forest.
Modified Causal Forests for Estimating Heterogeneous Causal Effects
Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers.
Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence
We consider 24 different DGPs, eleven different causal machine learning estimators, and three aggregation levels of the estimated effects.
Econometrics
