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MS Final Exam – Meghamala Sinha

Causal Structure Learning from Experiments and Observations

In this research, we present a novel way of combining data from multiple interventional experiments with observations to learn more accurate causal networks. While learning causal network by pooling data from different experiments is common, this paves the way for false causal discoveries, if the effects of interventions are uncertain. Our approach, called `Learn and Vote' learns causal links using data from each experiment and combines them by weighted averaging. We show through studies on synthetic and natural datasets that our method outperforms many state of the art approaches and is more robust with respect to modelling assumptions about the nature of the interventions.

Major Advisor: Prasad Tadepalli
Committee: Stephen Ramsey
Committee: Rebecca Hutchinson
Committee: Thomas Sharpton
GCR: Ren Guo

Friday, March 22, 2019 at 10:00am to 12:00pm

Kelley Engineering Center, 1126
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

Contact Email

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