Provable Guarantees on the Robustness of Decision Rules to Causal Interventions

TL;DR

This paper introduces a formal framework for assessing and guaranteeing the robustness of decision rules against causal interventions in Bayesian networks, providing efficient algorithms and interpretable bounds.

Contribution

It defines the interventional robustness problem for decision functions and offers tractable algorithms to compute bounds using arithmetic circuits.

Findings

Algorithms efficiently compute bounds on robustness probabilities.

Bounds are practical and interpretable for real-world networks.

Method enhances causal robustness in decision-making systems.

Abstract

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.