2 citations · 2 across the 5 of their papers we have counts for
6 papers
Gender and Discipline Shape Length, Content and Tone of Grant Peer Review Reports
Stefan Müller, Gabriel Okasa, Michaela Strinzel +3
Peer review by experts is central to the evaluation of grant proposals, but little is known about how gender and disciplinary differences shape the content and tone of grant peer r…
A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports
Gabriel Okasa, Alberto de León, Michaela Strinzel +4
Peer review in grant evaluation informs funding decisions, but the contents of peer review reports are rarely analyzed. In this work, we develop a thoroughly tested pipeline to ana…
Sample Fit Reliability
Gabriel Okasa, Kenneth A. Younge
Researchers frequently test and improve model fit by holding a sample constant and varying the model. We propose methods to test and improve sample fit by holding a model constant…
Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance
Gabriel Okasa
Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learn…
The Effect of Sport in Online Dating: Evidence from Causal Machine Learning
Daniel Boller, Michael Lechner, Gabriel Okasa
Online dating emerged as a key platform for human mating. Previous research focused on socio-demographic characteristics to explain human mating in online dating environments, negl…
Random Forest Estimation of the Ordered Choice Model
Michael Lechner, Gabriel Okasa
In this paper we develop a new machine learning estimator for ordered choice models based on the random forest. The proposed Ordered Forest flexibly estimates the conditional choic…