mlsanityData to 7 Oct 2026

D'ARTAGNAN: Counterfactual Video Generation

arXiv 2206.01651 ↗First posted 3 Jun 2022cs.CVcited by 27

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Abstract (arXiv)

Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions. We explore this path for the case of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We combine deep neural networks, twin causal networks and generative adversarial methods for the first time to build D'ARTAGNAN (Deep ARtificial Twin-Architecture GeNerAtive Networks), a novel causal generative model. We demonstrate the soundness of our approach on a synthetic dataset before applying it to cardiac ultrasound videos to answer the question: "What would this echocardiogram look like if the patient had a different ejection fraction?". To do so, we generate new ultrasound videos, retaining the video style and anatomy of the original patient, while modifying the Ejection Fraction conditioned on a given input. We achieve an SSIM score of 0.79 and an R2 score of 0.51 on the counterfactual videos. Code and models are available at: https://github.com/HReynaud/dartagnan.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 16 of its 37 references that are in the map. First posted on arXiv 3 Jun 2022.

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16 of 37 references.

References · 16

  1. 2017Neural Discrete Representation Learningcited by 4,650
  2. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,139
  3. 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
  4. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  5. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 210
  6. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  7. 2019Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Modelscited by 114
  8. 2021Counterfactual Generative Networkscited by 111
  9. 2018Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learningcited by 56
  10. 2018Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networkscited by 55
  11. 2020Counterfactual Representation Learning with Balancing Weightscited by 45
  12. 2021Ultrasound Video Transformers for Cardiac Ejection Fraction Estimationcited by 34
4 more
  1. 2021Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Imagescited by 13
  2. 2020XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantomscited by 11
  3. 20204D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Modelcited by 7
  4. 2018A nonparametric projection-based estimator for the probability of causation, with application to water sanitation in Kenyacited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 10; 2025: 8; 2026: 1.

27 papers.

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  1. 2023Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesiscited by 32
  2. 2024Benchmarking Counterfactual Image Generationcited by 22
  3. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  4. 2023Counterfactual Generative Models for Time-Varying Treatmentscited by 17
  5. 2024EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharingcited by 17
  6. 2024A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamicscited by 17
  7. 2023Causal models in string diagramscited by 14
  8. 2023DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRIcited by 14
  9. 2023CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomycited by 13
  10. 2025Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Modelscited by 12
  11. 2024Spatio-temporal neural distance fields for conditional generative modeling of the heartcited by 7
  12. 2024Contrastive representations of high-dimensional, structured treatmentscited by 7
15 more
  1. 2023Non-parametric identifiability and sensitivity analysis of synthetic control modelscited by 5
  2. 2024DiffExplainer: Unveiling Black Box Models Via Counterfactual Generationcited by 5
  3. 2025Causally Steered Diffusion for Automated Video Counterfactual Generationcited by 5
  4. 2024Spillover Detection for Donor Selection in Synthetic Control Modelscited by 4
  5. 2024Explainable and Controllable Motion Curve Guided Cardiac Ultrasound Video Generationcited by 4
  6. 2025EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generationcited by 4
  7. 2024Uncovering Hidden Subspaces in Video Diffusion Models Using Re-Identificationcited by 2
  8. 2024Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learningcited by 1
  9. 2025LD-ViCE: Latent Diffusion Model for Video Counterfactual Explanationscited by 1
  10. 2025Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanationscited by 1
  11. 2023Exploring the Hyperparameter Space of Image Diffusion Models for Echocardiogram Generationcited by 0
  12. 2025Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesiscited by 0
  13. 2025InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiographycited by 0
  14. 2025Quantum oracles give an advantage for identifying classical counterfactualscited by 0
  15. 2026Causal Transfer in Medical Image Analysiscited by 0