Semi-Supervised Learning for Deep Causal Generative Models
Yasin Ibrahim Hermione Warr Konstantinos Kamnitsas
Developing models that can answer questions of the form "How would $x$ change
if $y$ had been $z$?" is fundamental for 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 corresponding labels are available in 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.