Counterfactual Fairness with Partially Known Causal Graph
This addresses fairness in AI for sensitive groups by enabling causal fairness methods without full causal knowledge, though it is incremental as it builds on existing causal fairness frameworks.
The paper tackles the problem of achieving counterfactual fairness in machine learning when the true causal graph is unknown, proposing a method that uses partially known causal graphs and finds that fairness can be achieved as if the graph were fully known under specific conditions, with results demonstrated on simulated and real-world datasets.
Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrimination and bias through causal effects. Though causality-based fair learning is attracting increasing attention, current methods assume the true causal graph is fully known. This paper proposes a general method to achieve the notion of counterfactual fairness when the true causal graph is unknown. To be able to select features that lead to counterfactual fairness, we derive the conditions and algorithms to identify ancestral relations between variables on a \textit{Partially Directed Acyclic Graph (PDAG)}, specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. Interestingly, we find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided: the sensitive attributes do not have ancestors in the causal graph. Results on both simulated and real-world datasets demonstrate the effectiveness of our method.