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Found 12 papers, 3 papers with code
The Causal Impact of Credit Lines on Spending Distributions
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales.
Invariant Random Forest: Tree-Based Model Solution for OOD Generalization
This paper introduces a novel and effective solution for OOD generalization of decision tree models, named Invariant Decision Tree (IDT).
Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning
The dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns.
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning
A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty.
Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification
We seek an adaptive balance between the structural integrity and the flexibility for $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$, while Gaussian assumption results in a lack of flexibility for real data and highly flexible approaches (e. g., estimating the quantiles separately without a distribution structure) inevitably have drawbacks and may not lead to good generalization.
Decorr: Environment Partitioning for Invariant Learning and OOD Generalization
Invariant learning methods try to find an invariant predictor across several environments and have become popular in OOD generalization.
Robust Causal Learning for the Estimation of Average Treatment Effects
Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue.
Risk and return prediction for pricing portfolios of non-performing consumer credit
We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans.
Robust Orthogonal Machine Learning of Treatment Effects
Theoretically, the RCL estimators i) satisfy the (higher-order) orthogonal condition and are as \textit{consistent and doubly robust} as the DML estimators, and ii) get rid of the error-compounding issue.
The Causal Learning of Retail Delinquency
Classical estimators overlook the confounding effects and hence the estimation error can be magnificent.
Generative Learning of Heterogeneous Tail Dependence
We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data.
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns.
