Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data

Alicia Curth, Mihaela van der Schaar
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:7961-7980, 2023.

Abstract

We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. Albeit its great practical relevance, this problem has received little attention compared to its counterparts studying HTE estimation without time-to-event data or competing events. We take an outcome modeling approach to estimating HTEs, and consider how and when existing prediction models for time-to-event data can be used as plug-in estimators for potential outcomes. We then investigate whether competing events present new challenges for HTE estimation – in addition to the standard confounding problem –, and find that, because there are multiple definitions of causal effects in this setting – namely total, direct and separable effects –, competing events can act as an additional source of covariate shift depending on the desired treatment effect interpretation and associated estimand. We theoretically analyze and empirically illustrate when and how these challenges play a role when using generic machine learning prediction models for the estimation of HTEs.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-curth23a, title = {Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data}, author = {Curth, Alicia and van der Schaar, Mihaela}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {7961--7980}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/curth23a/curth23a.pdf}, url = {https://proceedings.mlr.press/v206/curth23a.html}, abstract = {We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. Albeit its great practical relevance, this problem has received little attention compared to its counterparts studying HTE estimation without time-to-event data or competing events. We take an outcome modeling approach to estimating HTEs, and consider how and when existing prediction models for time-to-event data can be used as plug-in estimators for potential outcomes. We then investigate whether competing events present new challenges for HTE estimation – in addition to the standard confounding problem –, and find that, because there are multiple definitions of causal effects in this setting – namely total, direct and separable effects –, competing events can act as an additional source of covariate shift depending on the desired treatment effect interpretation and associated estimand. We theoretically analyze and empirically illustrate when and how these challenges play a role when using generic machine learning prediction models for the estimation of HTEs.} }
Endnote
%0 Conference Paper %T Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data %A Alicia Curth %A Mihaela van der Schaar %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-curth23a %I PMLR %P 7961--7980 %U https://proceedings.mlr.press/v206/curth23a.html %V 206 %X We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. Albeit its great practical relevance, this problem has received little attention compared to its counterparts studying HTE estimation without time-to-event data or competing events. We take an outcome modeling approach to estimating HTEs, and consider how and when existing prediction models for time-to-event data can be used as plug-in estimators for potential outcomes. We then investigate whether competing events present new challenges for HTE estimation – in addition to the standard confounding problem –, and find that, because there are multiple definitions of causal effects in this setting – namely total, direct and separable effects –, competing events can act as an additional source of covariate shift depending on the desired treatment effect interpretation and associated estimand. We theoretically analyze and empirically illustrate when and how these challenges play a role when using generic machine learning prediction models for the estimation of HTEs.
APA
Curth, A. & van der Schaar, M.. (2023). Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:7961-7980 Available from https://proceedings.mlr.press/v206/curth23a.html.

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