Constrained Causal Bayesian Optimization

Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:304-321, 2023.

Abstract

We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cCBO first reduces the search space by exploiting the graph structure and, if available, an observational dataset; and then solves the restricted optimization problem by modelling target and constraint quantities using Gaussian processes and by sequentially selecting interventions via a constrained expected improvement acquisition function. We propose different surrogate models that enable to integrate observational and interventional data while capturing correlation among effects with increasing levels of sophistication. We evaluate cCBO on artificial and real-world causal graphs showing successful trade off between fast convergence and percentage of feasible interventions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-aglietti23a, title = {Constrained Causal {B}ayesian Optimization}, author = {Aglietti, Virginia and Malek, Alan and Ktena, Ira and Chiappa, Silvia}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {304--321}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/aglietti23a/aglietti23a.pdf}, url = {https://proceedings.mlr.press/v202/aglietti23a.html}, abstract = {We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cCBO first reduces the search space by exploiting the graph structure and, if available, an observational dataset; and then solves the restricted optimization problem by modelling target and constraint quantities using Gaussian processes and by sequentially selecting interventions via a constrained expected improvement acquisition function. We propose different surrogate models that enable to integrate observational and interventional data while capturing correlation among effects with increasing levels of sophistication. We evaluate cCBO on artificial and real-world causal graphs showing successful trade off between fast convergence and percentage of feasible interventions.} }
Endnote
%0 Conference Paper %T Constrained Causal Bayesian Optimization %A Virginia Aglietti %A Alan Malek %A Ira Ktena %A Silvia Chiappa %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-aglietti23a %I PMLR %P 304--321 %U https://proceedings.mlr.press/v202/aglietti23a.html %V 202 %X We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cCBO first reduces the search space by exploiting the graph structure and, if available, an observational dataset; and then solves the restricted optimization problem by modelling target and constraint quantities using Gaussian processes and by sequentially selecting interventions via a constrained expected improvement acquisition function. We propose different surrogate models that enable to integrate observational and interventional data while capturing correlation among effects with increasing levels of sophistication. We evaluate cCBO on artificial and real-world causal graphs showing successful trade off between fast convergence and percentage of feasible interventions.
APA
Aglietti, V., Malek, A., Ktena, I. & Chiappa, S.. (2023). Constrained Causal Bayesian Optimization. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:304-321 Available from https://proceedings.mlr.press/v202/aglietti23a.html.

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