Conference Video: Usefulness of Knowledge Graphs for Improving Data-Driven Causal Network Learning

This is Meghamala Sinha’s presentation from the WiDS Puget Sound Conference 2021. Enjoy!

ABSTRACT:

Causal network, useful for action planning, prediction and diagnosis, is difficult to learn solely from data in absence of prior knowledge. We demonstrate using large-scale, multi-graph called knowledge graph as prior in a score-based causal learning method with improved accuracy on real-world data.

BIO:

Meghamala Sinha is a PhD candidate at Oregon State University. She is majoring in Computer Science and minoring in Biological Data Science. Her research interest is Causal Inference and its application to data-driven areas like Machine Learning, AI, Intelligent Systems and Computational Biology. Her work centers around using fundamentals of Causality to differentiate true cause-effect relationships from mere associations in data and building a more robust and reliable inference model.