This paper presents a groundbreaking approach to counterfactual inference through quantile regression. By reframing the problem as an extended quantile regression task, the method eliminates the reliance on structural causal models, showcasing superior statistical efficiency compared to existing methods. Empirical results validate the effectiveness of this approach across various datasets.
Traditional approaches to counterfactual inference often require access to or estimation of structural causal models, which can be challenging. This paper proposes a practical framework that formulates counterfactual inference as an extended quantile regression problem implemented with neural networks under a bi-level optimization scheme. The method enhances generalization ability and provides an upper bound on generalization error.
Theoretical insights establish a fundamental relationship between counterfactual outcomes and quantiles, enabling the identification of counterfactual outcomes through quantile regression from factual observations under mild assumptions. The proposed method ensures identifiability even when structural causal models are not identifiable and eliminates the need to recover true noise values.
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by Shaoan Xie,B... о arxiv.org 02-29-2024
https://arxiv.org/pdf/2306.05751.pdfГлибші Запити