Transportability Without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets

Konstantina Lelova, Gregory F Cooper, Sofia Triantafillou
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:1765-1773, 2026.

Abstract

Transporting causal information across populations is a critical challenge in clinical decision-making. Causal modeling provides criteria for identifiability and transportability, but these require knowledge of the causal graph, which rarely holds in practice. We propose a Bayesian method that combines observational data from the target domain with experimental data from a different domain to identify s-admissible backdoor sets, which enable unbiased estimation of causal effects across populations, without requiring the causal graph. We prove that if such a set exists, we can always find one within the Markov boundary of the outcome, narrowing the search space, and we establish asymptotic convergence guarantees for our method. We develop a greedy algorithm that reframes transportability as a feature selection problem, selecting conditioning sets that maximize the marginal likelihood of experimental data given observational data. In simulated and semi-synthetic data, our method correctly identifies transportability bias, improves causal effect estimation, and performs favorably against alternatives.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-lelova26a, title = { Transportability Without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets }, author = {Lelova, Konstantina and Cooper, Gregory F and Triantafillou, Sofia}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {1765--1773}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/lelova26a/lelova26a.pdf}, url = {https://proceedings.mlr.press/v300/lelova26a.html}, abstract = { Transporting causal information across populations is a critical challenge in clinical decision-making. Causal modeling provides criteria for identifiability and transportability, but these require knowledge of the causal graph, which rarely holds in practice. We propose a Bayesian method that combines observational data from the target domain with experimental data from a different domain to identify s-admissible backdoor sets, which enable unbiased estimation of causal effects across populations, without requiring the causal graph. We prove that if such a set exists, we can always find one within the Markov boundary of the outcome, narrowing the search space, and we establish asymptotic convergence guarantees for our method. We develop a greedy algorithm that reframes transportability as a feature selection problem, selecting conditioning sets that maximize the marginal likelihood of experimental data given observational data. In simulated and semi-synthetic data, our method correctly identifies transportability bias, improves causal effect estimation, and performs favorably against alternatives. } }
Endnote
%0 Conference Paper %T Transportability Without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets %A Konstantina Lelova %A Gregory F Cooper %A Sofia Triantafillou %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-lelova26a %I PMLR %P 1765--1773 %U https://proceedings.mlr.press/v300/lelova26a.html %V 300 %X Transporting causal information across populations is a critical challenge in clinical decision-making. Causal modeling provides criteria for identifiability and transportability, but these require knowledge of the causal graph, which rarely holds in practice. We propose a Bayesian method that combines observational data from the target domain with experimental data from a different domain to identify s-admissible backdoor sets, which enable unbiased estimation of causal effects across populations, without requiring the causal graph. We prove that if such a set exists, we can always find one within the Markov boundary of the outcome, narrowing the search space, and we establish asymptotic convergence guarantees for our method. We develop a greedy algorithm that reframes transportability as a feature selection problem, selecting conditioning sets that maximize the marginal likelihood of experimental data given observational data. In simulated and semi-synthetic data, our method correctly identifies transportability bias, improves causal effect estimation, and performs favorably against alternatives.
APA
Lelova, K., Cooper, G.F. & Triantafillou, S.. (2026). Transportability Without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:1765-1773 Available from https://proceedings.mlr.press/v300/lelova26a.html.

Related Material