Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data

Matthew Engelhard, Ricardo Henao
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:9571-9581, 2022.

Abstract

The mixture cure model allows failure probability to be estimated separately from failure timing in settings wherein failure never occurs in a subset of the population. In this paper, we draw on insights from representation learning and causal inference to develop a neural network based mixture cure model that is free of distributional assumptions, yielding improved prediction of failure timing, yet still effectively disentangles information about failure timing from information about failure probability. Our approach also mitigates effects of selection biases in the observation of failure and censoring times on estimation of the failure density and censoring density, respectively. Results suggest this approach could be applied to distinguish factors predicting failure occurrence versus timing and mitigate biases in real-world observational datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-engelhard22a, title = { Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data }, author = {Engelhard, Matthew and Henao, Ricardo}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {9571--9581}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/engelhard22a/engelhard22a.pdf}, url = {https://proceedings.mlr.press/v151/engelhard22a.html}, abstract = { The mixture cure model allows failure probability to be estimated separately from failure timing in settings wherein failure never occurs in a subset of the population. In this paper, we draw on insights from representation learning and causal inference to develop a neural network based mixture cure model that is free of distributional assumptions, yielding improved prediction of failure timing, yet still effectively disentangles information about failure timing from information about failure probability. Our approach also mitigates effects of selection biases in the observation of failure and censoring times on estimation of the failure density and censoring density, respectively. Results suggest this approach could be applied to distinguish factors predicting failure occurrence versus timing and mitigate biases in real-world observational datasets. } }
Endnote
%0 Conference Paper %T Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data %A Matthew Engelhard %A Ricardo Henao %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-engelhard22a %I PMLR %P 9571--9581 %U https://proceedings.mlr.press/v151/engelhard22a.html %V 151 %X The mixture cure model allows failure probability to be estimated separately from failure timing in settings wherein failure never occurs in a subset of the population. In this paper, we draw on insights from representation learning and causal inference to develop a neural network based mixture cure model that is free of distributional assumptions, yielding improved prediction of failure timing, yet still effectively disentangles information about failure timing from information about failure probability. Our approach also mitigates effects of selection biases in the observation of failure and censoring times on estimation of the failure density and censoring density, respectively. Results suggest this approach could be applied to distinguish factors predicting failure occurrence versus timing and mitigate biases in real-world observational datasets.
APA
Engelhard, M. & Henao, R.. (2022). Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:9571-9581 Available from https://proceedings.mlr.press/v151/engelhard22a.html.

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