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Found 10 papers, 3 papers with code
Unveiling the Potential of Robustness in Evaluating Causal Inference Models
The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE).
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization
In this study, we address this gap by introducing SpecFormer, specifically designed to enhance ViTs' resilience against adversarial attacks, with support from carefully derived theoretical guarantees.
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.
Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
Multivariate sequential data collected in practice often exhibit temporal irregularities, including nonuniform time intervals and component misalignment.
Deep into The Domain Shift: Transfer Learning through Dependence Regularization
This paper proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals.
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.
Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information
In this paper, we propose a moderately-balanced representation learning (MBRL) framework based on recent covariates balanced representation learning methods and orthogonal machine learning theory.
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.
Understanding Distributional Ambiguity via Non-robust Chance Constraint
Without the boundedness constraint, the CCO problem is shown to perform uniformly better than the DRO problem, irrespective of the radius of the ambiguity set, the choice of the divergence measure, or the tail heaviness of the center distribution.
