Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Directed cyclic graphical representations of feedback models
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Modeling discrete interventional data using directed cyclic graphical models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Introduction to Causal Inference
The Journal of Machine Learning Research
Variable construction for predictive and causal modeling of online education data
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Strong faithfulness and uniform consistency in causal inference
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Cyclic causal models with discrete variables: Markov Chain equilibrium semantics and sample ordering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Directed acyclic graphs have been used fruitfully to represent causal structures (Pearl 1988). However, in the social sciences and elsewhere models are often used which correspond both causally and statistically to directed graphs with directed cycles (Spirtes 1995). Pearl (1993) discussed predicting the effects of intervention in models of this kind, so-called linear nonrecursive structural equation models. This raises the question of whether it is possible to make inferences about causal structure with cycles, from sample data. In particular do there exist general, informative, feasible and reliable procedures for inferring causal structure from conditional independence relations among variables in a sample generated by an unknown causal structure? In this paper I present a discovery algorithm that is correct in the large sample limit, given commonly (but often implicitly) made plausible assumptions, and which provides information about the existence or non-existence of causal pathways from one variable to another. The algorithm is polynomial on sparse graphs.