Causal Bounds and Observable Constraints for Non-deterministic Models
Causal Bounds and Observable Constraints for Non-deterministic Models
复制标题
非确定性模型的因果界限和可观察约束
DOI：
10.5555/2503308.2188414
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Roland R. Ramsahai
中科院分区：
文献类型：
--
作者：
Roland R. Ramsahai
包含潜在变量的条件独立关系不一定意味着可观察的独立性。它们可能意味着对可观测参数和因果界的不等式约束，可用于证伪和识别。计算此类约束的文献通常涉及在反事实框架中确定的底层数据生成过程。如果分析师不了解潜在机制的本质，那么他们可能希望使用一个允许潜在机制是概率性的模型。本文给出了一种没有任何决定论的弱模型的计算方法，并演示了工具变量模型的计算方法，尽管它适用于其他模型。该方法基于在决策理论框架中对凸多面体映射的分析，并且可以在现成的多面体计算软件中实现。已知的约束和界限被复制到概率模型中，新的约束和界限被计算为工具变量模型，分别没有随机化、排除限制和单调性假设的非确定性版本。
Conditional independence relations involving latent variables do not necessarily imply observable independences. They may imply inequality constraints on observable parameters and causal bounds, which can be used for falsification and identification. The literature on computing such constraints often involve a deterministic underlying data generating process in a counterfactual framework. If an analyst is ignorant of the nature of the underlying mechanisms then they may wish to use a model which allows the underlying mechanisms to be probabilistic. A method of computation for a weaker model without any determinism is given here and demonstrated for the instrumental variable model, though applicable to other models. The approach is based on the analysis of mappings with convex polytopes in a decision theoretic framework and can be implemented in readily available polyhedral computation software. Well known constraints and bounds are replicated in a probabilistic model and novel ones are computed for instrumental variable models without non-deterministic versions of the randomization, exclusion restriction and monotonicity assumptions respectively.
影响因子：
0.9
作者：
Kaufman, Sol;Kaufman, Jay S.;MacLehose, Richard F.
通讯作者：
MacLehose, Richard F.
影响因子：
2
作者：
SOMMER, A;ZEGER, SL
通讯作者：
ZEGER, SL