Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation

Lucile Ter-Minassian, Liran Szlak, Ehud Karavani, Christopher C. Holmes, Yishai Shimoni
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:1900-1908, 2025.

Abstract

Modelling causal effects from observational data for deciding policy actions can benefit from being interpretable and transparent; both due to the high stakes involved and the inherent lack of ground truth labels to evaluate the accuracy of such models. To date, attempts at transparent causal effect estimation consist of applying post hoc explanation methods to black-box models, which are not interpretable. Here, we present BICauseTree: an interpretable balancing method that identifies clusters where natural experiments occur locally. Our approach builds on decision trees with a customized objective function to improve balancing and reduce treatment allocation bias. Consequently, it can additionally detect subgroups presenting positivity violations, exclude them, and provide a covariate-based definition of the target population we can infer from and generalize to. We evaluate the method’s performance using synthetic and realistic datasets, explore its bias-interpretability tradeoff, and show that it is comparable with existing approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-ter-minassian25a, title = {Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation}, author = {Ter-Minassian, Lucile and Szlak, Liran and Karavani, Ehud and Holmes, Christopher C. and Shimoni, Yishai}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {1900--1908}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/ter-minassian25a/ter-minassian25a.pdf}, url = {https://proceedings.mlr.press/v258/ter-minassian25a.html}, abstract = {Modelling causal effects from observational data for deciding policy actions can benefit from being interpretable and transparent; both due to the high stakes involved and the inherent lack of ground truth labels to evaluate the accuracy of such models. To date, attempts at transparent causal effect estimation consist of applying post hoc explanation methods to black-box models, which are not interpretable. Here, we present BICauseTree: an interpretable balancing method that identifies clusters where natural experiments occur locally. Our approach builds on decision trees with a customized objective function to improve balancing and reduce treatment allocation bias. Consequently, it can additionally detect subgroups presenting positivity violations, exclude them, and provide a covariate-based definition of the target population we can infer from and generalize to. We evaluate the method’s performance using synthetic and realistic datasets, explore its bias-interpretability tradeoff, and show that it is comparable with existing approaches.} }
Endnote
%0 Conference Paper %T Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation %A Lucile Ter-Minassian %A Liran Szlak %A Ehud Karavani %A Christopher C. Holmes %A Yishai Shimoni %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-ter-minassian25a %I PMLR %P 1900--1908 %U https://proceedings.mlr.press/v258/ter-minassian25a.html %V 258 %X Modelling causal effects from observational data for deciding policy actions can benefit from being interpretable and transparent; both due to the high stakes involved and the inherent lack of ground truth labels to evaluate the accuracy of such models. To date, attempts at transparent causal effect estimation consist of applying post hoc explanation methods to black-box models, which are not interpretable. Here, we present BICauseTree: an interpretable balancing method that identifies clusters where natural experiments occur locally. Our approach builds on decision trees with a customized objective function to improve balancing and reduce treatment allocation bias. Consequently, it can additionally detect subgroups presenting positivity violations, exclude them, and provide a covariate-based definition of the target population we can infer from and generalize to. We evaluate the method’s performance using synthetic and realistic datasets, explore its bias-interpretability tradeoff, and show that it is comparable with existing approaches.
APA
Ter-Minassian, L., Szlak, L., Karavani, E., Holmes, C.C. & Shimoni, Y.. (2025). Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:1900-1908 Available from https://proceedings.mlr.press/v258/ter-minassian25a.html.

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