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02/04/2022
To Impute or not to Impute? – Missing Data in Treatment Effect Estimation
Missing data is a systemic problem in practical scenarios that causes no...
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11/04/2021
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Missing data is an important problem in machine learning practice. Start...
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10/25/2021
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
Machine learning models have been criticized for reflecting unfair biase...
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02/11/2021
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge
Selecting causal inference models for estimating individualized treatmen...
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09/28/2020
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Regularization improves generalization of supervised models to out-of-sa...
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11/27/2019
Improving Model Robustness Using Causal Knowledge
For decades, researchers in fields, such as the natural and social scien...
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10/30/2018
MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis
With an aging and growing population, the number of women requiring eith...
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07/16/2018