Projects

Phantom relationships often emerge from the combination of multiple data sources. While confounding contaminates causal relationships, it also contains the information needed to identify and remove its own impact. Assuming a bound on the cardinality of a discrete universal confounder turns the problem into a mixture model, allowing identification of within-source probability distributions. 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.