arXiv subjects

Gabriel Okasa

Publications and source records attributed to Gabriel Okasa.

6 recordsLinked to original sources

arXiv2025

Gender and Discipline Shape Length, Content and Tone of Grant Peer Review Reports

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 review reports. We analyzed 39,280 review reports submitted to the Swiss National Science Foundation between 2016 and 2023, covering 11,385 proposals for project funding across 21 disciplines from the Social Sciences and Humanities (SSH), Life Sciences (LS), and Mathematics, Informatics, Natural Sciences, and Technology (MINT). Using supervised machine learning, we classified over 1.3 million sentences by evaluation criteria and sentiment. Reviews in SSH were significantly longer and more critical, with less focus on the applicant's track record, while those in MINT were more concise and positive, with a higher focus on the track record, as compared to those in LS. Compared to male reviewers, female reviewers write longer reviews that more closely align with the evaluation criteria and express more positive sentiments. Female applicants tend to receive reviews with slightly more positive sentiment than male applicants. Gender and disciplinary culture influence how grant proposals are reviewed - shaping the tone, length, and focus of peer review reports. These differences have important implications for fairness and consistency in research funding.

physics.soc-ph↗

arXiv2024

A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports

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 analyze the texts of grant peer review reports using methods from applied Natural Language Processing (NLP) and machine learning. We start by developing twelve categories reflecting content of grant peer review reports that are of interest to research funders. This is followed by multiple human annotators' iterative annotation of these categories in a novel text corpus of grant peer review reports submitted to the Swiss National Science Foundation. After validating the human annotation, we use the annotated texts to fine-tune pre-trained transformer models to classify these categories at scale, while conducting several robustness and validation checks. Our results show that many categories can be reliably identified by human annotators and machine learning approaches. However, the choice of text classification approach considerably influences the classification performance. We also find a high correspondence between out-of-sample classification performance and human annotators' perceived difficulty in identifying categories. Our results and publicly available fine-tuned transformer models will allow researchers and research funders and anybody interested in peer review to examine and report on the contents of these reports in a structured manner. Ultimately, we hope our approach can contribute to ensuring the quality and trustworthiness of grant peer review.

econ.EM↗

arXiv2022

Sample Fit Reliability

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 and varying the sample. Much as the bootstrap is a well-known method to re-sample data and estimate the uncertainty of the fit of parameters in a model, we develop Sample Fit Reliability (SFR) as a set of computational methods to re-sample data and estimate the reliability of the fit of observations in a sample. SFR uses Scoring to assess the reliability of each observation in a sample, Annealing to check the sensitivity of results to removing unreliable data, and Fitting to re-weight observations for more robust analysis. We provide simulation evidence to demonstrate the advantages of using SFR, and we replicate three empirical studies with treatment effects to illustrate how SFR reveals new insights about each study.

econ.EM↗

arXiv2022

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. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with non-linearities and near-multicollinearity. An empirical application contrasts the estimation of marginal effects and their standard errors with an ordered logit model. A software implementation of the Ordered Forest is provided both in R and Python in the package orf available on CRAN and PyPI, respectively.

econ.EM↗

arXiv2022

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

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-learners for estimation of heterogeneous treatment effects under the usage of sample-splitting and cross-fitting to reduce the overfitting bias. In both synthetic and semi-synthetic simulations we find that the performance of the meta-learners in finite samples greatly depends on the estimation procedure. The results imply that sample-splitting and cross-fitting are beneficial in large samples for bias reduction and efficiency of the meta-learners, respectively, whereas full-sample estimation is preferable in small samples. Furthermore, we derive practical recommendations for application of specific meta-learners in empirical studies depending on particular data characteristics such as treatment shares and sample size.

econ.EM↗

arXiv2021

The Effect of Sport in Online Dating: Evidence from Causal Machine Learning

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, neglecting the commonly recognized relevance of sport. This research investigates the effect of sport activity on human mating by exploiting a unique data set from an online dating platform. Thereby, we leverage recent advances in the causal machine learning literature to estimate the causal effect of sport frequency on the contact chances. 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. For female users, we do not find evidence for such an effect. In addition, for male users the effect increases with higher income.

econ.GN↗