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Found 6 papers, 2 papers with code
Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values
In this contribution, we propose machine learning techniques to predict zombie firms.
What is essential is visible to the eye: Saliency in primary school ranking and its effect on academic achievements
We propose a new strategy to identify the impact of class rank, exploiting a "visible" primary school rank from teachers' exam grades, and an "invisible" rank from unreported standardized test scores.
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
Estimation of subgroup-specific causal effects is performed via a two-stage approach for which we provide theoretical guarantees.
Supervised learning for the prediction of firm dynamics
Thanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics.
Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach
This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e. g., under an irregular assignment mechanism).
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms
This paper provides a link between causal inference and machine learning techniques - specifically, Classification and Regression Trees (CART) - in observational studies where the receipt of the treatment is not randomized, but the assignment to the treatment can be assumed to be randomized (irregular assignment mechanism).
