Cover Image

Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sullivan, Hué, Christophe, Hurlin, Christophe, Pérignon, Sébastien, Saurin
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Econometrics Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Fairness of Credit Scoring Models
by: Hurlin, Christophe, et al.
Published: (2022)

Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing
by: Schwarz, Philipp Alexander, et al.
Published: (2025)

Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction
by: Zeng, Hao, et al.
Published: (2024)

Reverse Stress Testing Geopolitical Risk in Corporate Credit Portfolios: A Formal and Operational Framework
by: Hurlin, Christophe, et al.
Published: (2026)

Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
by: Athey, Susan, et al.
Published: (2016)

Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators
by: Klaassen, Sven, et al.
Published: (2025)

Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso
by: Hué, Sullivan, et al.
Published: (2025)

A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
by: Parikh, Harsh, et al.
Published: (2025)

Adaptive Principal Component Regression with Applications to Panel Data
by: Agarwal, Anish, et al.
Published: (2023)

LASSO Inference for High Dimensional Predictive Regressions
by: Gao, Zhan, et al.
Published: (2024)

Conformal Prediction: a Unified Review of Theory and New Challenges
by: Fontana, Matteo, et al.
Published: (2020)

Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
by: Baiardi, Anna, et al.
Published: (2026)

ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
by: Kato, Masahiro
Published: (2025)

Statistical Inference for Score Decompositions
by: Dimitriadis, Timo, et al.
Published: (2026)

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
by: Athey, Susan, et al.
Published: (2023)

Conformal Prediction for Nonparametric Instrumental Regression
by: Kato, Masahiro
Published: (2026)

Spatially Robust Inference with Predicted and Missing at Random Labels
by: Salerno, Stephen, et al.
Published: (2026)

Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
by: More, Sushant, et al.
Published: (2024)

Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data
by: Langevin, Raphaël
Published: (2026)

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
by: Kato, Masahiro
Published: (2026)

Dealing with Logs and Zeros in Regression Models
by: Benatia, David, et al.
Published: (2022)

Linear Multidimensional Regression with Interactive Fixed-Effects
by: Freeman, Hugo
Published: (2022)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects
by: Kallus, Nathan, et al.
Published: (2022)

Long-term Causal Inference Under Persistent Confounding via Data Combination
by: Imbens, Guido, et al.
Published: (2022)

Double Robust Bayesian Inference on Average Treatment Effects
by: Breunig, Christoph, et al.
Published: (2022)

Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
by: Agarwal, Anish, et al.
Published: (2022)

Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
by: Nagai, Tomoko, et al.
Published: (2024)

Formalising causal inference as prediction on a target population
by: Höltgen, Benedikt, et al.
Published: (2024)

Functional effects models: Accounting for preference heterogeneity in panel data with machine learning
by: Salvadé, Nicolas, et al.
Published: (2025)

Vector Copula Variational Inference and Dependent Block Posterior Approximations
by: Fu, Yu, et al.
Published: (2025)

Robust Matrix Estimation with Side Information
by: Agarwal, Anish, et al.
Published: (2026)

Fast Online Changepoint Detection
by: Ghezzi, Fabrizio, et al.
Published: (2024)

Regularizing Extrapolation in Causal Inference
by: Arbour, David, et al.
Published: (2025)

Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints
by: Sakaguchi, Shosei
Published: (2021)

Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
by: Si, Nian
Published: (2023)

A step towards the integration of machine learning and classic model-based survey methods
by: Żądło, Tomasz, et al.
Published: (2024)

Machine-Learning-Assisted Comparison of Regression Functions
by: Yan, Jian, et al.
Published: (2025)

Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
by: Agarwal, Abhineet, et al.
Published: (2023)

An Introduction to Double/Debiased Machine Learning
by: Ahrens, Achim, et al.
Published: (2025)

Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
by: Chen, Hongyu, et al.
Published: (2026)