Cover Image

Generally-Occurring Model Change for Robust Counterfactual Explanations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Ao, Wu, Tieru
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Flexible Counterfactual Explanations with Generative Models
by: Hellemans, Stig, et al.
Published: (2025)

Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms
by: Xu, Ao, et al.
Published: (2024)

Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
by: Wielopolski, Patryk, et al.
Published: (2024)

FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
by: Fragkathoulas, Christos, et al.
Published: (2024)

Joint Distribution-Informed Shapley Values for Sparse Counterfactual Explanations
by: You, Lei, et al.
Published: (2024)

CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations
by: Motzkus, Franz, et al.
Published: (2024)

Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
by: Furman, Oleksii, et al.
Published: (2024)

Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
by: Komanduri, Aneesh, et al.
Published: (2024)

Axiomatic Foundations of Counterfactual Explanations
by: Amgoud, Leila, et al.
Published: (2026)

Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causality
by: Li, Jiangmeng, et al.
Published: (2024)

Counterfactual Probabilistic Diffusion with Expert Models
by: Mu, Wenhao, et al.
Published: (2025)

Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
by: Li, He, et al.
Published: (2025)

ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
by: De Moor, Jakob, et al.
Published: (2025)

Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection
by: Zhang, Jiarui, et al.
Published: (2024)

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
by: Stępka, Ignacy, et al.
Published: (2024)

Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
by: Diallo, Alec F., et al.
Published: (2024)

De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
by: Zhao, Yonghe, et al.
Published: (2023)

Provably Robust Bayesian Counterfactual Explanations under Model Changes
by: Duell, Jamie, et al.
Published: (2026)

Robust Stochastic Graph Generator for Counterfactual Explanations
by: Prado-Romero, Mario Alfonso, et al.
Published: (2023)

Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
by: Shen, ChengAo, et al.
Published: (2024)

Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept
by: Trilla, Alexandre, et al.
Published: (2024)

From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
by: Shmueli, Galit, et al.
Published: (2025)

Partial Identification Approach to Counterfactual Fairness Assessment
by: Rho, Saeyoung, et al.
Published: (2025)

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
by: Xu, Yichen
Published: (2026)

CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
by: Yi, Huiyang, et al.
Published: (2026)

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens
by: Lakkaraju, Kausik, et al.
Published: (2024)

Interval Abstractions for Robust Counterfactual Explanations
by: Jiang, Junqi, et al.
Published: (2024)

TRUST: Transparent, Robust and Ultra-Sparse Trees
by: Dorador, Albert
Published: (2025)

The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
by: Yi, Huiyang, et al.
Published: (2025)

Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks
by: Hua, Wenyue, et al.
Published: (2024)

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
by: Agarwal, Abhineet, et al.
Published: (2023)

Towards Interpretable Deep Generative Models via Causal Representation Learning
by: Moran, Gemma E., et al.
Published: (2025)

Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
by: Rahman, Md Musfiqur, et al.
Published: (2024)

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
by: Lemos, Pablo, et al.
Published: (2024)

M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
by: Sun, Zexu, et al.
Published: (2024)

RobustX: Robust Counterfactual Explanations Made Easy
by: Jiang, Junqi, et al.
Published: (2025)

Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
by: Rudner, Tim G. J., et al.
Published: (2024)

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective
by: Huang, Chengpiao, et al.
Published: (2025)

Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance
by: Choudhury, Jyotishka Ray, et al.
Published: (2023)

Partially Observed Structural Causal Models
by: Orujlu, Turan, et al.
Published: (2026)