27 citations · 64 across the 4 of their papers we have counts for
4 papers
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Trent Kyono, Yao Zhang, Alexis Bellot +1
Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a…
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
Boris van Breugel, Trent Kyono, Jeroen Berrevoets +1
Machine learning models have been criticized for reflecting unfair biases in the training data. Instead of solving for this by introducing fair learning algorithms directly, we foc…
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge
Trent Kyono, Ioana Bica, Zhaozhi Qian +1
Selecting causal inference models for estimating individualized treatment effects (ITE) from observational data presents a unique challenge since the counterfactual outcomes are ne…
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono, Yao Zhang, Mihaela van der Schaar
Regularization improves generalization of supervised models to out-of-sample data. Prior works have shown that prediction in the causal direction (effect from cause) results in low…