Nested Counterfactual Identification from Arbitrary Surrogate Experiments
利用任意替代实验识别嵌套反事实AI 解读 ↓
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利用任意替代实验识别嵌套反事实
- 研究问题
- 当可用信息由不同的观测分布和实验分布组合而成时，哪些嵌套反事实能够识别，如何系统处理涉及中介或公平性的嵌套查询？
- 方法与思路
- 作者提出反事实去嵌套定理，将嵌套反事实映射为非嵌套形式，再给出图形识别条件及针对观测与实验分布组合的识别算法。
- 材料陈述的贡献
- 摘要称，论文提供识别的充分必要图形条件与高效、完备算法；在其理论框架内，算法无法返回表达式意味着查询不可识别。
- 阅读关注点
- 应核对反事实定义、图模型允许的结构与可用实验信息、去嵌套定理的适用条件，以及算法完备性和复杂度的证明范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明完整模型假设，必要充分性与算法失败的含义须在该框架内理解。
原文摘要
The Ladder of Causation describes three qualitatively different types of activities an agent may be interested in engaging in, namely, seeing (observational), doing (interventional), and imagining (counterfactual) (Pearl and Mackenzie, 2018). The inferential challenge imposed by the causal hierarchy is that data is collected by an agent observing or intervening in a system (layers 1 and 2), while its goal may be to understand what would have happened had it taken a different course of action, contrary to what factually ended up happening (layer 3). While there exists a solid understanding of the conditions under which cross-layer inferences are allowed from observations to interventions, the results are somewhat scarcer when targeting counterfactual quantities. In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically, building on a more explicit definition of nested counterfactuals, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones. For instance, applications in mediation and fairness analysis usually evoke notions of direct, indirect, and spurious effects, which naturally require nesting. Second, we introduce a sufficient and necessary graphical condition for counterfactual identification from an arbitrary combination of observational and experimental distributions. Lastly, we develop an efficient and complete algorithm for identifying nested counterfactuals; failure of the algorithm returning an expression for a query implies it is not identifiable.