bibtext: @InProceedings{ Ibr_SemiSupervised_MICCAI2024,
                   author = { Ibrahim, Yasin and Warr, Hermione and Kamnitsas, Konstantinos },
                   title = { { Semi-Supervised Learning for Deep Causal Generative Models } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
abstract: Developing models that are capable of answering
  questions of the form “How would x change if y had been z?” is fundamental to advancing medical image analysis. Training causal generative models that address such counterfactual questions, though, currently requires that all relevant variables have been observed and that the corresponding labels are available in the training data. However, clinical data may not have complete records for all patients and state of the art causal generative models are unable to take full advantage of this. We thus develop, for the first time, a semi-supervised deep causal generative model that exploits the causal relationships between variables to maximise the use of all available data. We explore this in the setting where each sample is either fully labelled or fully unlabelled, as well as the more clinically realistic case of having different labels missing for each sample. We leverage techniques from causal inference to infer missing values and subsequently generate realistic counterfactuals, even for samples with incomplete labels. Code is available at: https://github.com/yi249/ssl-causal
title: Semi-Supervised Learning for Deep Causal Generative Models
authors: Ibrahim, Yasin Warr, Hermione Kamnitsas, Konstantinos