Alternative Measures of Direct and Indirect Effects

Jose M. Peña
Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:1-19, 2024.

Abstract

There are a number of measures of direct and indirect effects in the literature on causality. These are suitable in some cases and unsuitable in others. We describe a case where the existing measures are unsuitable and propose new suitable ones. We also show that the new measures can partially handle unmeasured treatment-outcome confounding, and bound long-term effects by combining experimental and observational data. We also introduce the concepts of indirect benefit and harm (i.e., through a mediator), and use our new measure to quantify them.

Cite this Paper

BibTeX
@InProceedings{pmlr-v246-pena24a, title = {Alternative Measures of Direct and Indirect Effects}, author = {Pe\~na, Jose M.}, booktitle = {Proceedings of The 12th International Conference on Probabilistic Graphical Models}, pages = {1--19}, year = {2024}, editor = {Kwisthout, Johan and Renooij, Silja}, volume = {246}, series = {Proceedings of Machine Learning Research}, month = {11--13 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v246/main/assets/pena24a/pena24a.pdf}, url = {https://proceedings.mlr.press/v246/pena24a.html}, abstract = {There are a number of measures of direct and indirect effects in the literature on causality. These are suitable in some cases and unsuitable in others. We describe a case where the existing measures are unsuitable and propose new suitable ones. We also show that the new measures can partially handle unmeasured treatment-outcome confounding, and bound long-term effects by combining experimental and observational data. We also introduce the concepts of indirect benefit and harm (i.e., through a mediator), and use our new measure to quantify them.} }
Endnote
%0 Conference Paper %T Alternative Measures of Direct and Indirect Effects %A Jose M. Peña %B Proceedings of The 12th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2024 %E Johan Kwisthout %E Silja Renooij %F pmlr-v246-pena24a %I PMLR %P 1--19 %U https://proceedings.mlr.press/v246/pena24a.html %V 246 %X There are a number of measures of direct and indirect effects in the literature on causality. These are suitable in some cases and unsuitable in others. We describe a case where the existing measures are unsuitable and propose new suitable ones. We also show that the new measures can partially handle unmeasured treatment-outcome confounding, and bound long-term effects by combining experimental and observational data. We also introduce the concepts of indirect benefit and harm (i.e., through a mediator), and use our new measure to quantify them.
APA
Peña, J.M.. (2024). Alternative Measures of Direct and Indirect Effects. Proceedings of The 12th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 246:1-19 Available from https://proceedings.mlr.press/v246/pena24a.html.

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