2021/05/19 by Benjie Wang, Clare Lyle, Wang, Benjie +3 · 1 citation
Computer Science · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian network #Bayesian probability #Computer science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine learning #Robustness (evolution) #Software deployment #Theoretical computer science #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2105.09108
published in arXiv (Cornell University) (Cornell University) · 21 pages (8+13 Appendix). To be published in IJCAI 2021
arxiv created 2021/05/19 · openalex publication_date 2021/05/19 · arxiv updated 2021/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. Such shifts can be viewed as interventions on a causal graph, which capture (possibly hypothetical) changes in the data-generating process, whether due to natural reasons or by the action of an adversary. We consider causal Bayesian networks and formally define the interventional robustness problem, a novel model-based notion of robustness for decision functions that measures worst-case performance with respect to a set of interventions that denote changes to parameters and/or causal influences. By relying on a tractable representation of Bayesian networks as arithmetic circuits, we provide efficient algorithms for computing guaranteed upper and lower bounds on the interventional robustness probabilities. Experimental results demonstrate that the methods yield useful and interpretable bounds for a range of practical networks, paving the way towards provably causally robust decision-making systems.