Event Details:
Tuesday, June 7, 2022
8:30am - 9:30am PDT
This event is open to:
General Public
Free and open to the public
Tuesday, June 7, 2022 [Link to join] (ID: 996 2837 2037, Password: 386638)
- Speaker: Mona Azadkia (ETH)
- Title: A Fast Non-parametric Approach for Causal Structure Learning in Polytrees
- Discussant: Bryon Aragam (Chicago Booth)
- Abstract: We study the problem of causal structure learning with no assumptions on the functional relationships and noise. We develop DAG-FOCI, a computationally fast algorithm for this setting that is based on the FOCI variable selection algorithm in (Azadkia 2021). DAG-FOCI requires no tuning parameter and outputs the parents and the Markov boundary of a response variable of interest. We provide high-dimensional guarantees of our procedure when the underlying graph is a polytree. Furthermore, we demonstrate the applicability of DAG-FOCI on real data from computational biology (Sachs et al., 2005) and illustrate the robustness of our methods to violations of assumptions.
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