Estimation of Personalized Effects Associated With Causal Pathways

Razieh Nabi, Phyllis Kanki, Ilya Shpitser
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:672-681, 2018.

Abstract

The goal of personalized decision making is to map a unit’s characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway may be of interest as well. For example, we may wish to maximize the chemical effect of a drug given data from an observational study where the chemical effect of the drug on the outcome is entangled with the indirect effect mediated by differential adherence. In such cases, we may wish to optimize the direct effect of a drug, while keeping the indirect effect to that of some reference treatment. [15] shows how to combine mediation analysis and dynamic treatment regime ideas to defines policies associated with causal pathways and counterfactual responses to these policies. In this paper, we derive a variety of methods for learning high quality policies of this type from data, in a causal model corresponding to a longitudinal setting of practical importance. We illustrate our methods via a dataset of HIV patients undergoing therapy, gathered in the Nigerian PEPFAR program.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-nabi18a, title = {Estimation of Personalized Effects Associated With Causal Pathways}, author = {Nabi, Razieh and Kanki, Phyllis and Shpitser, Ilya}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {672--681}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/nabi18a/nabi18a.pdf}, url = {https://proceedings.mlr.press/r16/nabi18a.html}, abstract = {The goal of personalized decision making is to map a unit’s characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway may be of interest as well. For example, we may wish to maximize the chemical effect of a drug given data from an observational study where the chemical effect of the drug on the outcome is entangled with the indirect effect mediated by differential adherence. In such cases, we may wish to optimize the direct effect of a drug, while keeping the indirect effect to that of some reference treatment. [15] shows how to combine mediation analysis and dynamic treatment regime ideas to defines policies associated with causal pathways and counterfactual responses to these policies. In this paper, we derive a variety of methods for learning high quality policies of this type from data, in a causal model corresponding to a longitudinal setting of practical importance. We illustrate our methods via a dataset of HIV patients undergoing therapy, gathered in the Nigerian PEPFAR program.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Estimation of Personalized Effects Associated With Causal Pathways %A Razieh Nabi %A Phyllis Kanki %A Ilya Shpitser %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-nabi18a %I PMLR %P 672--681 %U https://proceedings.mlr.press/r16/nabi18a.html %V R16 %X The goal of personalized decision making is to map a unit’s characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway may be of interest as well. For example, we may wish to maximize the chemical effect of a drug given data from an observational study where the chemical effect of the drug on the outcome is entangled with the indirect effect mediated by differential adherence. In such cases, we may wish to optimize the direct effect of a drug, while keeping the indirect effect to that of some reference treatment. [15] shows how to combine mediation analysis and dynamic treatment regime ideas to defines policies associated with causal pathways and counterfactual responses to these policies. In this paper, we derive a variety of methods for learning high quality policies of this type from data, in a causal model corresponding to a longitudinal setting of practical importance. We illustrate our methods via a dataset of HIV patients undergoing therapy, gathered in the Nigerian PEPFAR program. %Z Reissued by PMLR on 04 October 2026.
APA
Nabi, R., Kanki, P. & Shpitser, I.. (2018). Estimation of Personalized Effects Associated With Causal Pathways. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:672-681 Available from https://proceedings.mlr.press/r16/nabi18a.html. Reissued by PMLR on 04 October 2026.

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