Partially Intervenable Causal Models
仅允许部分变量干预的因果模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要核心是受限干预下的因果效应识别、潜在结果、干预图与 do-calculus，直接属于因果推断理论。
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仅允许部分变量干预的因果模型
- 研究问题
- 当只有部分变量上的干预具有明确含义时，如何构建图形因果模型，并形成与这种限制相容的完整因果效应识别理论？
- 方法与思路
- 作者以单世界干预图连接图形模型和潜在结果，定义受限干预模型，推广 do-calculus，并引入马尔可夫核概率运算公理。
- 材料陈述的贡献
- 摘要称给出此类模型的完整识别理论和干预演算，并由此获得决策理论图形因果框架中受限干预效应的完整识别理论。
- 阅读关注点
- 应核对允许干预集合的定义、图形语义、完备性结论的范围，以及推广演算如何处理无法定义干预的变量。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实。摘要未展示公理、证明或应用实例，无法据此判断具体研究中的效应是否可识别。
原文摘要
Graphical causal models led to the development of complete non-parametric identification theory in arbitrary structured systems, and general approaches to efficient inference. Nevertheless, graphical approaches to causal inference have not been embraced by the statistics and public health communities. In those communities causal assumptions are instead expressed in terms of potential outcomes, or responses to hypothetical interventions. Such interventions are generally conceptualized only on a limited set of variables, where the corresponding experiment could, in principle, be performed. By contrast, graphical approaches to causal inference generally assume interventions on all variables are well defined - an overly restrictive and unrealistic assumption that may have limited the adoption of these approaches in applied work in statistics and public health. In this paper, we build on a unification of graphical and potential outcomes approaches to causality exemplified by Single World Intervention Graphs (SWIGs) to define graphical models with a restricted set of allowed interventions. We give a complete identification theory for such models, and develop a complete calculus of interventions based on a generalization of the do-calculus, and axioms that govern probabilistic operations on Markov kernels. A corollary of our results is a complete identification theory for causal effects in another graphical framework with a restricted set of interventions, the decision theoretic graphical formulation of causality.