Cover Image

MCCE: Missingness-aware Causal Concept Explainer

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gao, Jifan, Chen, Guanhua
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MCCE: Monte Carlo sampling of realistic counterfactual explanations
by: Redelmeier, Annabelle, et al.
Published: (2021)

MCCE: A Framework for Multi-LLM Collaborative Co-Evolution
by: Ran, Nian, et al.
Published: (2025)

TraNCE: Transformative Non-linear Concept Explainer for CNNs
by: Akpudo, Ugochukwu Ejike, et al.
Published: (2025)

Causal representation learning from network data
by: Zhang, Jifan, et al.
Published: (2025)

Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
by: Liu, Qiao
Published: (2026)

A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts
by: Wanga, Yingni, et al.
Published: (2022)

Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset
by: Taylor-Melanson, Will, et al.
Published: (2024)

Nearly Optimal Bayesian Inference for Structural Missingness
by: Liang, Chen, et al.
Published: (2026)

MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records
by: Wang, Zekai, et al.
Published: (2024)

Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
by: Zheng, Wugeng, et al.
Published: (2026)

Factorized Explainer for Graph Neural Networks
by: Huang, Rundong, et al.
Published: (2023)

Domain Adaptation Under MNAR Missingness
by: Stokes, Tyrel, et al.
Published: (2025)

Missingness Bias Calibration in Feature Attribution Explanations
by: Sridhar, Shailesh, et al.
Published: (2026)

Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness
by: Liang, Zihan, et al.
Published: (2025)

Axiomatic Explainer Globalness via Optimal Transport
by: Hill, Davin, et al.
Published: (2024)

Robust Explainer Recommendation for Time Series Classification
by: Nguyen, Thu Trang, et al.
Published: (2023)

Boundary-Aware Uncertainty for Feature Attribution Explainers
by: Hill, Davin, et al.
Published: (2022)

Quantifying Potential Observation Missingness in Inverse Reinforcement Learning
by: Benac, Leo, et al.
Published: (2026)

Implet: A Post-hoc Subsequence Explainer for Time Series Models
by: Meng, Fanyu, et al.
Published: (2025)

Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
by: Genans, Ferdinand, et al.
Published: (2026)

Fair Graph Machine Learning under Adversarial Missingness Processes
by: Lina, Debolina Halder, et al.
Published: (2023)

A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation
by: Chen, Xingran, et al.
Published: (2025)

Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
by: Wen, Hechuan, et al.
Published: (2024)

GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV
by: Giesa, Niklas, et al.
Published: (2024)

Beyond Random Missingness: Clinically Rethinking for Healthcare Time Series Imputation
by: Qian, Linglong, et al.
Published: (2024)

Exploiting Missing Data Remediation Strategies using Adversarial Missingness Attacks
by: Koyuncu, Deniz, et al.
Published: (2024)

Understanding Missingness in Time-series Electronic Health Records for Individualized Representation
by: Ghosheh, Ghadeer O., et al.
Published: (2024)

T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
by: Ortigossa, Evandro S., et al.
Published: (2024)

MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts
by: Lee, Jihye, et al.
Published: (2025)

COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
by: Fournier, Gregoire, et al.
Published: (2025)

Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
by: Khan, Zulqarnain, et al.
Published: (2022)

CoDy: Counterfactual Explainers for Dynamic Graphs
by: Qu, Zhan, et al.
Published: (2024)

Imputation of Unknown Missingness in Sparse Electronic Health Records
by: Han, Jun, et al.
Published: (2026)

Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs
by: Wendland, Joshua, et al.
Published: (2026)

Learning Conformal Explainers for Image Classifiers
by: Alkhatib, Amr, et al.
Published: (2025)

Structural Disentanglement of Causal and Correlated Concepts
by: Zhao, Qilong, et al.
Published: (2024)

BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
by: Sloneker, Whitney, et al.
Published: (2024)

Concept-SAE: Active Causal Probing of Visual Model Behavior
by: Ding, Jianrong, et al.
Published: (2025)

Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness
by: Mao, Xiaojun, et al.
Published: (2024)

Blockwise Missingness meets AI: A Tractable Solution for Semiparametric Inference
by: Xu, Qi, et al.
Published: (2025)