提出新方法填补生物实验中未测药物-细胞互作空白
General Causal Imputation via Synthetic Interventions
- 基于合成干预思想，构建更通用的因果补全框架
- 在真实与模拟数据上表现优于已有两种方法
- 适合处理复杂生物系统中缺失交互数据
针对细胞类型与药物化合物之间仅部分已知互作的问题，本文提出一种新的因果补全方法——广义合成干预（GSI）。该方法在更复杂的潜在因子模型下证明了可识别性，并在合成及真实数据上验证其性能优于现有两种基于合成干预的估计器（SI-A 和 SI-C）。结果表明，GSI 能有效恢复或超越已有方法的预测能力，为生物医学研究中的未观测互作推断提供新工具。
原文摘要 · Abstract (English)
Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their interactions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.
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