All unsupervised techniques rely on distributional assumptions in order to recover components or clusters. Discrete data poses an interesting challenge in that categorical distributions are inherently non-parametric, prohibiting the use of parametric assumptions. One alternative approach, which I have pioneered, is the use of the use of \emph{causal structures} to help separate unconfounded components in data. Such an approach is natural, as confounded systems are an inherently causal problem. This perspective expands the notion of causal identifiability, as many graphically unidentifiable relationships can be identified.

It is not unusual for studies and experts to disagree with each other. Such disagreement is often driven by differing contexts rather than incorrect deductions or bad data. One way to investigate contradiction is to use merged data to build a larger picture. Unfortunately, many private medical settings deny direct access to data. Through decision fusion, I seek to understand when results from different settings are in conflict and what these disagreements can indicate about the underlying system.

Features often contain a mixture of “good” and “bad” information. From a fairness standpoint, SAT scores contain information about both inherent academic ability, and also access to tutoring resources. From a domain adaptation standpoint, some information may have stable and reliable relationships with the prediction label, while other relationships break down. My work uses insights from causal inference to determine data-representations that sort between the different components of information that are hidden in these ambiguous features.

We process non-coding regions of the genome which contain duplication and mutation signatures. These mutation profiles have been shown to be predictive of various forms of cancer.