Decision Support for Disability Employment using Counterfactual Survival Analysis
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(Published version)
Date
2022
Authors
Xuan Tran, H.
Duy Le, T.
Li, J.
Liu, L.
Liu, J.
Zhao, Y.
Waters, T.
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Conference paper
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Proceedings 2022 IEEE International Conference on Big Data Big Data 2022, 2022, pp.2103-2112
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2022 IEEE International Conference on Big Data (Big Data) (17 Dec 2022 - 20 Dec 2022 : Osaka)
Abstract
In Disability Employment Service (DES), DES providers are confronted with "what-if"questions to assist workers with disability in deciding which skill should be improved to increase their job retention time. For instance, what would happen to the job retention time of a worker with disability if he improved his computer skill to an advanced level? This requires counterfactual inference to estimate the counterfactuals of the survival outcome, i.e., job retention time, under different skill improvement scenarios. While exiting survival analysis techniques are not designed for counterfactual problems, current counterfactual prediction methods are assumed to work with non-survival outcomes. In this paper, we propose the Counterfactual Survival Network (CSN), a representation learning based method for counterfactual survival prediction, where both confounding and censoring biases are removed based on latent representations. Since ground truth counterfactuals are unavailable, we develop a sample specific estimator to estimate counterfactuals for training samples. These estimated counterfactual outcomes are used as pseudo ground truth to train the counterfactual prediction model. We demonstrate the benefits of our method in decision support tasks with the case study of Australian workers and three public datasets. Results show that CSN can help Australian workers with disability increase their job retention time. Our method also shows its promising performance in other applications
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Copyright 2022 IEEE
Access Condition Notes: Accepted manuscript available on Open Access