  In this position and problem pitch paper, we offer a solution to the
reference class problem in causal inference. We apply the Reconcile algorithm
for model multiplicity in machine learning to reconcile heterogeneous effects
in causal inference. Discrepancy between conditional average treatment effect
(CATE) estimators of heterogeneous effects poses the reference class problem,
where estimates for individual predictions differ by choice of reference class.
By adopting the individual to group framework for interpreting probability, we
can recognize that the reference class problem -- which appears across fields
such as philosophy of science and causal inference -- is equivalent to the
model multiplicity problem in computer science. We then apply the Reconcile
Algorithm to reconcile differences in estimates of individual probability among
CATE estimators. Because the reference class problem manifests in contexts of
individual probability prediction using group-based evidence, our results have
tangible implications for ensuring fair outcomes in high-stakes such as
healthcare, insurance, and housing, especially for marginalized communities. By
highlighting the importance of mitigating disparities in predictive modeling,
our work invites further exploration into interdisciplinary strategies that
combine technical rigor with a keen awareness of social implications.
Ultimately, our findings advocate for a holistic approach to algorithmic
fairness, underscoring the critical role of thoughtful, well-rounded solutions
in achieving the broader goals of equity and access.
