Search Results for author: Trent Kyono

Found 7 papers, 3 papers with code

To Impute or not to Impute? Missing Data in Treatment Effect Estimation

no code implementations4 Feb 2022 Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar

Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects.

Imputation

Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge

no code implementations11 Feb 2021 Trent Kyono, Ioana Bica, Zhaozhi Qian, Mihaela van der Schaar

We leverage the invariance of causal structures across domains to propose a novel model selection metric specifically designed for ITE methods under the UDA setting.

Causal Inference Model Selection +1

Improving Model Robustness Using Causal Knowledge

no code implementations27 Nov 2019 Trent Kyono, Mihaela van der Schaar

We show in this paper how prior knowledge in the form of a causal graph can be utilized to guide model selection, i. e., to identify from a set of trained networks the models that are the most robust and invariant to unseen domains.

Model Selection

Siamese Survival Analysis with Competing Risks

no code implementations ICLR 2018 Anton Nemchenko, Trent Kyono, Mihaela van der Schaar

Survival analysis in the presence of multiple possible adverse events, i. e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.).

Survival Analysis

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