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Found 9 papers, 4 papers with code
Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Data
The proposed distributionally robust estimators are established using the Inverse Probability Weighting (IPW) method extended from the discrete one for policy evaluation and learning under continuous treatments.
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
To address these challenges, this paper introduces a Distributionally Robust Metric (DRM) for CATE estimator selection.
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.
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.
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 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.
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.
Inquisitive Question Generation for High Level Text Comprehension
Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems.
