Latent Clustered Causal Models
Name
Yun-annieyun-meng-eecs-2021-thesis.pdf
Description
Thesis PDF
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959.52 KB
Format
Adobe PDF
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c9c3c83463c187d22503effea4b03081
Author(s)
Yun, Annie
Advisor(s)
Uhler, Caroline
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
Abstract
We consider the problem of learning directed graphical models in the presence of latent variables. We define latent clustered causal models as a particular restriction on directed graphical models with latent variables and corresponding clusters of observed nodes, characterized by edges between only observed and latent variables. We discuss this model’s particular applicability towards genomics applications and examine its relationship to prior causal structure recovery work. We show identifiability results on this model and design a consistent three-stage algorithm that discovers clusters of observed nodes, a partial ordering over clusters, and finally, the entire structure over both observed and latent nodes. We also evaluate our method on synthetic datasets and demonstrate its performance in low sample-size regimes.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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