Causal Discovery on Dependent Mixed Data with Applications to Gene Regulatory Network Inference
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相依混合数据的因果发现及其在基因调控网络推断中的应用
- 研究问题
- 当观测样本相互依赖，且变量同时包含连续与离散类型时，如何从观测数据恢复因果图并改善基因调控关系推断？
- 方法与思路
- 作者构建含潜变量和跨单位相关高斯误差的结构方程模型，以成对最大似然估计协方差，用 EM 补全潜变量后去相关。
- 材料陈述的贡献
- 摘要称，去相关后的潜在数据可用于标准因果发现；模拟改善图恢复，单细胞应用改善测试预测似然且部分边获文献支持。
- 阅读关注点
- 应核对去相关变换是否保留目标因果结构、离散测量模型与误差相关设定，以及图恢复评价和文献支持的验证方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未说明因果识别条件、改善幅度及预测表现与因果正确性的关系。
原文摘要
Causal discovery aims to infer causal relationships among variables from observational data, typically represented by a directed acyclic graph (DAG). Most existing methods assume independent and identically distributed observations, an assumption often violated in modern applications. In addition, many datasets contain a mixture of continuous and discrete variables, which further complicates causal modeling when dependence across samples is present. To address these challenges, we propose a de-correlation framework for causal discovery from dependent mixed data. Our approach formulates a structural equation model with latent variables that accommodates both continuous and discrete variables while allowing correlated Gaussian errors across units. We estimate the dependence structure among samples via a pairwise maximum likelihood estimator for the covariance matrix and develop an EM algorithm to impute latent variables underlying discrete observations. A de-correlation transformation of the recovered latent data enables the use of standard causal discovery algorithms to learn the underlying causal graph. Simulation studies demonstrate that the proposed method substantially improves causal graph recovery compared with applying standard methods directly to the original dependent data. We apply our approach to single-cell RNA sequencing data to infer gene regulatory networks governing embryonic stem cell differentiation. The inferred regulatory networks show significantly improved predictive likelihood on test data, and many high-confidence edges are supported by known regulatory interactions reported in the literature.