用反事实逻辑从有选择偏差的实验数据中恢复真实因果效应
Recover Experimental Data with Selection Bias using Counterfactual Logic
- 通过结构因果模型构建反事实世界，分析选择机制传播路径
- 证明实验分布在选择偏差下仍可完全恢复，给出图形化判定准则
- 适合需要处理偏差实验数据的因果推断研究者使用
选择偏差源于样本系统性地被纳入或排除，严重影响因果推断的有效性。尽管Bareinboim等人提出利用部分外部信息从偏差观测数据中恢复无偏的观测与干预分布，但其后门调整方法复杂且高度依赖观测数据，在实际场景中应用受限。本文首次形式化证明了在实验数据下，$P(Y^*_{x^*})$ 可在选择偏差条件下实现可恢复性。通过显式构建结构因果模型（SCMs）中的反事实世界，分析观测世界的选择机制如何传递至反事实域，推导出完整的图形化与理论判别准则，证明实验分布不受选择偏差影响。进一步提出基于部分无偏观测数据，从有偏差的实验数据集中恢复 $P(Y^*_{x^*})$ 的系统方法。模拟研究复现真实科研场景，验证了该方法的实用性，为实际因果推断中缓解选择偏差提供了明确指导。
原文摘要 · Abstract (English)
Selection bias, arising from the systematic inclusion or exclusion of certain samples, poses a significant challenge to the validity of causal inference. While Bareinboim et al. introduced methods for recovering unbiased observational and interventional distributions from biased data using partial external information, the complexity of the backdoor adjustment and the method's strong reliance on observational data limit its applicability in many practical settings. In this paper, we formally discover the recoverability of $P(Y^*_{x^*})$ under selection bias with experimental data. By explicitly constructing counterfactual worlds via Structural Causal Models (SCMs), we analyze how selection mechanisms in the observational world propagate to the counterfactual domain. We derive a complete set of graphical and theoretical criteria to determine that the experimental distribution remain unaffected by selection bias. Furthermore, we propose principled methods for leveraging partially unbiased observational data to recover $P(Y^*_{x^*})$ from biased experimental datasets. Simulation studies replicating realistic research scenarios demonstrate the practical utility of our approach, offering concrete guidance for mitigating selection bias in applied causal inference.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。