Causal generalized linear models via Pearson risk invariance
通过 Pearson 风险不变性识别因果广义线性模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确围绕因果父节点发现与广义线性因果模型识别展开，单环境识别也是其直接讨论的因果问题。
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通过 Pearson 风险不变性识别因果广义线性模型
- 研究问题
- 在多种异质环境数据难以获得时，能否利用目标变量的广义线性结构识别其因果父节点，尤其从单一环境完成识别？
- 方法与思路
- 作者结合因果模型下 Pearson 风险不变性与条件期望似然最大化两项性质搜索模型，并为高维变量系统提出逐步贪心搜索。
- 材料陈述的贡献
- 摘要称这些性质可唯一刻画因果模型；对离散参数已知的泊松和逻辑回归，可从单一环境识别，并提供 R 包实现。
- 阅读关注点
- 应核对唯一识别所需的完整条件、已知离散参数的作用，以及贪心搜索能否找到目标模型和有限样本误选表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出完整识别条件，单环境识别结论不能推广至任意因果系统。
原文摘要
Prediction invariance of causal models under heterogeneous settings has been exploited by a number of recent methods for causal discovery, typically focussing on recovering the causal parents of a target variable of interest. Existing methods require observational data from a number of sufficiently different environments, which is rarely available. In this paper, we consider a structural equation model where the target variable is described by a generalized linear model conditional on its parents. Besides having finite moments, no modelling assumptions are made on the conditional distributions of the other variables in the system, and nonlinear effects on the target variable can naturally be accommodated by a generalized additive structure. Under this setting, we characterize the causal model uniquely by means of two key properties: the Pearson risk invariant under the causal model and, conditional on the causal parents, the causal parameters maximize the expected likelihood. These two properties form the basis of a computational strategy for searching the causal model among all possible models. A stepwise greedy search is proposed for systems with a large number of variables. Crucially, for generalized linear models with a known dispersion parameter, such as Poisson and logistic regression, the causal model can be identified from a single data environment. The method is implemented in the R package causalreg.