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.

通过采用 Reconcile 算法解决了因果推断中的参考类问题，通过协调异质效应中的模型多样性，解决了个体概率估计中的差异，从而确保公平结果，并强调了算法公正性的整体方法的重要性。

调和因果推断中的异质效应

Reconciling Heterogeneous Effects in Causal Inference

Conditional Average Treatment Effects (CATE) estimation is one of the main
challenges in causal inference with observational data. In addition to Machine
Learning based-models, nonparametric estimators called meta-learners have been
developed to estimate the CATE with the main advantage of not restraining the
estimation to a specific supervised learning method. This task becomes,
however, more complicated when the treatment is not binary as some limitations
of the naive extensions emerge. This paper looks into meta-learners for
estimating the heterogeneous effects of multi-valued treatments. We consider
different meta-learners, and we carry out a theoretical analysis of their error
upper bounds as functions of important parameters such as the number of
treatment levels, showing that the naive extensions do not always provide
satisfactory results. We introduce and discuss meta-learners that perform well
as the number of treatments increases. We empirically confirm the strengths and
weaknesses of those methods with synthetic and semi-synthetic datasets.

本文研究了针对多值处理的异质效应的元学习方法，考虑不同的元学习算法，以及它们在不同的参数下的误差上界，提出并讨论了能够适用于不同处理多样性的方法，并使用合成半合成数据集验证了这些方法的优劣。