ICML 2025

Causal Logistic Bandits with Counterfactual Fairness Constraints
Jiajun Chen 1 Jin Tian 2 Christopher John Quinn 1
Abstract
Artificial intelligence will play a significant role
in decision making in numerous aspects of soci-
ety. Numerous fairness criteria have been pro-
posed in the machine learning community, but
there remains limited investigation into fairness
as defined through specified attributes in a sequen-
tial decision-making framework. In this paper, we
focus on causal logistic bandit problems where
the learner seeks to make fair decisions, under
a notion of fairness that accounts for counterfac-
tual reasoning. We propose and analyze an al-
gorithm by leveraging primal-dual optimization
for constrained causal logistic bandits where the
non-linear constraints are a priori unknown and
must be learned in time. We obtain sub-linear
regret guarantees with leading term similar to
that for unconstrained logistic bandits (Lee et al.,
2024) while guaranteeing sub-linear constraint
violations. We show how to achieve zero cumula-
tive constraint violations with a small increase in
the regret bound.
1. Introduction
Artificial intelligence (AI) models, using techniques from
statistics and machine learning, are increasingly being used
to make decisions that affect people’s lives. In light of this, a
plethora of formal fairness criteria have been proposed (Dar-
lington, 1971; Dwork et al., 2012; Hardt et al., 2016; Zhang
et al., 2016; Kusner et al., 2017; Zafar et al., 2017; Nabi
& Shpitser, 2018; Chiappa, 2019; Chouldechova & Roth,
2020; Imai & Jiang, 2023; Plecko & Bareinboim, 2024).
There has been growing interest in the sequential decision-
making community for accounting for fairness, including in
settings such as classic and contextual bandits (Joseph et al.,
2018), combinatorial bandits (Xu et al., 2020), bandits with
1Department of Computer Science, Iowa State University,
Ames, IA, USA 2Mohamed bin Zayed University of Artificial
Intelligence, Abu Dhabi, UAE. C...