Identification of Personalized Effects Associated With Causal Pathways

Ilya Shpitser, Eli Sherman
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:529-538, 2018.

Abstract

Unlike classical causal inference, where the goal is to estimate average causal effects within a population, in settings such as per- sonalized medicine, the goal is to map a unit’s characteristics to a treatment tailored to maxi- mize the expected outcome for that unit. Ob- taining high-quality mappings of this type is the goal of the dynamic treatment regime liter- ature. In healthcare settings, optimizing poli- cies with respect to a particular causal pathway is often of interest as well. In the context of average treatment effects, estimation of effects associated with causal pathways is considered in the mediation analysis literature. In this paper, we combine mediation analy- sis and dynamic treatment regime ideas and consider how unit characteristics may be used to tailor a treatment strategy that maximizes an effect along specified sets of causal path- ways. In particular, we define counterfactual responses to such policies, give a general iden- tification algorithm for these counterfactuals, and prove completeness of the algorithm for unrestricted policies. A corollary of our re- sults is that the identification algorithm for re- sponses to policies given in [16] is complete for arbitrary policies.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-shpitser18a, title = {Identification of Personalized Effects Associated With Causal Pathways}, author = {Shpitser, Ilya and Sherman, Eli}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {529--538}, 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/shpitser18a/shpitser18a.pdf}, url = {https://proceedings.mlr.press/r16/shpitser18a.html}, abstract = {Unlike classical causal inference, where the goal is to estimate average causal effects within a population, in settings such as per- sonalized medicine, the goal is to map a unit’s characteristics to a treatment tailored to maxi- mize the expected outcome for that unit. Ob- taining high-quality mappings of this type is the goal of the dynamic treatment regime liter- ature. In healthcare settings, optimizing poli- cies with respect to a particular causal pathway is often of interest as well. In the context of average treatment effects, estimation of effects associated with causal pathways is considered in the mediation analysis literature. In this paper, we combine mediation analy- sis and dynamic treatment regime ideas and consider how unit characteristics may be used to tailor a treatment strategy that maximizes an effect along specified sets of causal path- ways. In particular, we define counterfactual responses to such policies, give a general iden- tification algorithm for these counterfactuals, and prove completeness of the algorithm for unrestricted policies. A corollary of our re- sults is that the identification algorithm for re- sponses to policies given in [16] is complete for arbitrary policies.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Identification of Personalized Effects Associated With Causal Pathways %A Ilya Shpitser %A Eli Sherman %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-shpitser18a %I PMLR %P 529--538 %U https://proceedings.mlr.press/r16/shpitser18a.html %V R16 %X Unlike classical causal inference, where the goal is to estimate average causal effects within a population, in settings such as per- sonalized medicine, the goal is to map a unit’s characteristics to a treatment tailored to maxi- mize the expected outcome for that unit. Ob- taining high-quality mappings of this type is the goal of the dynamic treatment regime liter- ature. In healthcare settings, optimizing poli- cies with respect to a particular causal pathway is often of interest as well. In the context of average treatment effects, estimation of effects associated with causal pathways is considered in the mediation analysis literature. In this paper, we combine mediation analy- sis and dynamic treatment regime ideas and consider how unit characteristics may be used to tailor a treatment strategy that maximizes an effect along specified sets of causal path- ways. In particular, we define counterfactual responses to such policies, give a general iden- tification algorithm for these counterfactuals, and prove completeness of the algorithm for unrestricted policies. A corollary of our re- sults is that the identification algorithm for re- sponses to policies given in [16] is complete for arbitrary policies. %Z Reissued by PMLR on 04 October 2026.
APA
Shpitser, I. & Sherman, E.. (2018). Identification of Personalized Effects Associated With Causal Pathways. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:529-538 Available from https://proceedings.mlr.press/r16/shpitser18a.html. Reissued by PMLR on 04 October 2026.

Related Material