  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.
