Identifying optimal sequential decisions

A. Philip Dawid, Vanessa Didelez
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:113-120, 2008.

Abstract

We consider conditions that allow us to find an optimal strategy for sequential decisions from a given data situation. For the case where all interventions are unconditional (atomic), identifiability has been discussed by Pearl & Robins (1995). We argue here that an optimal strategy must be conditional, i.e. take the information available at each decision point into account. We show that the identification of an optimal sequential decision strategy is more restrictive, in the sense that conditional interventions might not always be identified when atomic interventions are. We further demonstrate that a simple graphical criterion for the identifiability of an optimal strategy can be given.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-dawid08a, title = {Identifying optimal sequential decisions}, author = {Dawid, A. Philip and Didelez, Vanessa}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {113--120}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/dawid08a/dawid08a.pdf}, url = {https://proceedings.mlr.press/r6/dawid08a.html}, abstract = {We consider conditions that allow us to find an optimal strategy for sequential decisions from a given data situation. For the case where all interventions are unconditional (atomic), identifiability has been discussed by Pearl & Robins (1995). We argue here that an optimal strategy must be conditional, i.e. take the information available at each decision point into account. We show that the identification of an optimal sequential decision strategy is more restrictive, in the sense that conditional interventions might not always be identified when atomic interventions are. We further demonstrate that a simple graphical criterion for the identifiability of an optimal strategy can be given.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Identifying optimal sequential decisions %A A. Philip Dawid %A Vanessa Didelez %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-dawid08a %I PMLR %P 113--120 %U https://proceedings.mlr.press/r6/dawid08a.html %V R6 %X We consider conditions that allow us to find an optimal strategy for sequential decisions from a given data situation. For the case where all interventions are unconditional (atomic), identifiability has been discussed by Pearl & Robins (1995). We argue here that an optimal strategy must be conditional, i.e. take the information available at each decision point into account. We show that the identification of an optimal sequential decision strategy is more restrictive, in the sense that conditional interventions might not always be identified when atomic interventions are. We further demonstrate that a simple graphical criterion for the identifiability of an optimal strategy can be given. %Z Reissued by PMLR on 09 October 2024.
APA
Dawid, A.P. & Didelez, V.. (2008). Identifying optimal sequential decisions. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:113-120 Available from https://proceedings.mlr.press/r6/dawid08a.html. Reissued by PMLR on 09 October 2024.

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