On Measuring Causal Contributions via do-interventions

Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Bloebaum, Elias Bareinboim
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10476-10501, 2022.

Abstract

Causal contributions measure the strengths of different causes to a target quantity. Understanding causal contributions is important in empirical sciences and data-driven disciplines since it allows to answer practical queries like “what are the contributions of each cause to the effect?” In this paper, we develop a principled method for quantifying causal contributions. First, we provide desiderata of properties axioms that causal contribution measures should satisfy and propose the do-Shapley values (inspired by do-interventions [Pearl, 2000]) as a unique method satisfying these properties. Next, we develop a criterion under which the do-Shapley values can be efficiently inferred from non-experimental data. Finally, we provide do-Shapley estimators exhibiting consistency, computational feasibility, and statistical robustness. Simulation results corroborate with the theory.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-jung22a, title = {On Measuring Causal Contributions via do-interventions}, author = {Jung, Yonghan and Kasiviswanathan, Shiva and Tian, Jin and Janzing, Dominik and Bloebaum, Patrick and Bareinboim, Elias}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {10476--10501}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/jung22a/jung22a.pdf}, url = {https://proceedings.mlr.press/v162/jung22a.html}, abstract = {Causal contributions measure the strengths of different causes to a target quantity. Understanding causal contributions is important in empirical sciences and data-driven disciplines since it allows to answer practical queries like “what are the contributions of each cause to the effect?” In this paper, we develop a principled method for quantifying causal contributions. First, we provide desiderata of properties axioms that causal contribution measures should satisfy and propose the do-Shapley values (inspired by do-interventions [Pearl, 2000]) as a unique method satisfying these properties. Next, we develop a criterion under which the do-Shapley values can be efficiently inferred from non-experimental data. Finally, we provide do-Shapley estimators exhibiting consistency, computational feasibility, and statistical robustness. Simulation results corroborate with the theory.} }
Endnote
%0 Conference Paper %T On Measuring Causal Contributions via do-interventions %A Yonghan Jung %A Shiva Kasiviswanathan %A Jin Tian %A Dominik Janzing %A Patrick Bloebaum %A Elias Bareinboim %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-jung22a %I PMLR %P 10476--10501 %U https://proceedings.mlr.press/v162/jung22a.html %V 162 %X Causal contributions measure the strengths of different causes to a target quantity. Understanding causal contributions is important in empirical sciences and data-driven disciplines since it allows to answer practical queries like “what are the contributions of each cause to the effect?” In this paper, we develop a principled method for quantifying causal contributions. First, we provide desiderata of properties axioms that causal contribution measures should satisfy and propose the do-Shapley values (inspired by do-interventions [Pearl, 2000]) as a unique method satisfying these properties. Next, we develop a criterion under which the do-Shapley values can be efficiently inferred from non-experimental data. Finally, we provide do-Shapley estimators exhibiting consistency, computational feasibility, and statistical robustness. Simulation results corroborate with the theory.
APA
Jung, Y., Kasiviswanathan, S., Tian, J., Janzing, D., Bloebaum, P. & Bareinboim, E.. (2022). On Measuring Causal Contributions via do-interventions. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:10476-10501 Available from https://proceedings.mlr.press/v162/jung22a.html.

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