University of Pittsburgh

Using neural networks to inform causal structure when inputs cause outputs

PhD student
Date: 
Friday, November 16, 2018 - 12:30pm - 1:00pm

In this work, we explore the extent to which deep neural networks can be used to find causal structure when the variables in a dataset can be split into two sets and the causal direction between these two sets is known. More concretely, we used data where the inputs were known to cause the outputs, (i.e, the causal direction between inputs and outputs is known), but the latent structure is unknown. We present results on multiple simulated datasets and plan to use these algorithms to explore biological data, specifically cancer data, in the future. Cancer data will be used as a motivating example throughout the talk.

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