Reconciling Heterogeneous Effects in Causal Inference

TL;DR

This paper addresses the reference class problem in causal inference by applying the Reconcile algorithm to harmonize heterogeneous effects, with implications for fairness in high-stakes decision-making.

Contribution

It introduces the use of the Reconcile algorithm to resolve model multiplicity in causal inference, linking it to fairness and social implications.

Findings

Reconcile algorithm effectively harmonizes heterogeneous effect estimates.

The approach has implications for fairness in healthcare, insurance, and housing.

Highlights the importance of mitigating disparities in predictive modeling.

Abstract

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.