mlsanityData to 14 Oct 2026

Explaining Visual Models by Causal Attribution

arXiv 1909.08891 ↗First posted 19 Sep 2019stat.ML cs.LG cs.NEcited by 18

Not on the map. Why?

Abstract (arXiv)

Model explanations based on pure observational data cannot compute the effects of features reliably, due to their inability to estimate how each factor alteration could affect the rest. We argue that explanations should be based on the causal model of the data and the derived intervened causal models, that represent the data distribution subject to interventions. With these models, we can compute counterfactuals, new samples that will inform us how the model reacts to feature changes on our input. We propose a novel explanation methodology based on Causal Counterfactuals and identify the limitations of current Image Generative Models in their application to counterfactual creation.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Sep 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 8; 2023: 0; 2024: 3; 2025: 2; 2026: 1.

18 papers.

Sort:

By area:

  1. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  2. 2022Causality-based Neural Network Repaircited by 47
  3. 2022Causal Reasoning Meets Visual Representation Learning: A Prospective Studycited by 24
  4. 2021VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queriescited by 19
  5. 2022Deep Structural Causal Shape Modelscited by 11
  6. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  7. 2021Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanationcited by 8
  8. 2021Causal Learning for Socially Responsible AIcited by 7
  9. 2025Practical do-Shapley Explanations with Estimand-Agnostic Causal Inferencecited by 6
  10. 2022Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spacescited by 4
  11. 2024Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Datasetcited by 4
  12. 2024Counterfactual Explanations for Deep Learning-Based Traffic Forecastingcited by 3
6 more
  1. 2024Causal Feature Selection for Responsible Machine Learningcited by 2
  2. 2022Explanatory causal effects for model agnostic explanationscited by 1
  3. 2022Provable Robust Saliency-based Explanationscited by 1
  4. 2026CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactualscited by 1
  5. 2022Causality-Aware Local Interpretable Model-Agnostic Explanationscited by 0
  6. 2025A Framework for Causal Concept-based Model Explanationscited by 0