
ConstraintBased Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
While feedback loops are known to play important roles in many complex s...
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A Discovery Algorithm for Directed Cyclis Graphs
Directed acyclic graphs have been used fruitfully to represent causal st...
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Causal Inference Theory with Information Dependency Models
Inferring the potential consequences of an unobserved event is a fundame...
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Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
We prove the main rules of causal calculus (also called docalculus) for...
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Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Discovery of causal relations from observational data is essential for m...
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Robust Causal Estimation in the LargeSample Limit without Strict Faithfulness
Causal effect estimation from observational data is an important and muc...
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Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps
Fuzzy cognitive maps (FCMs) model feedback causal relations in interwove...
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ConstraintBased Causal Discovery In The Presence Of Cycles
While feedback loops are known to play important roles in many complex systems (for example, in economical, biological, chemical, physical, control and climatological systems), their existence is ignored in most of the causal discovery literature, where systems are typically assumed to be acyclic from the outset. When applying causal discovery algorithms designed for the acyclic setting on data generated by a system that involves feedback, one would not expect to obtain correct results, even in the infinitesample limit. In this work, we show that—surprisingly—the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback. More specifically, we prove that for observational data generated by a simple and σfaithful Structural Causal Model (SCM), FCI can be used to consistently estimate (i) the presence and absence of causal relations, (ii) the presence and absence of direct causal relations, (iii) the absence of confounders, and (iv) the absence of specific cycles in the causal graph of the SCM.
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