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Found 4 papers, 0 papers with code
Nonlinearity, Feedback and Uniform Consistency in Causal Structural Learning
The goal of Causal Discovery is to find automated search methods for learning causal structures from observational data.
A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption
Kalisch and B\"{u}hlmann (2007) showed that for linear Gaussian models, under the Causal Markov Assumption, the Strong Causal Faithfulness Assumption, and the assumption of causal sufficiency, the PC algorithm is a uniformly consistent estimator of the Markov Equivalence Class of the true causal DAG for linear Gaussian models; it follows from this that for the identifiable causal effects in the Markov Equivalence Class, there are uniformly consistent estimators of causal effects as well.
Causal Clustering for 1-Factor Measurement Models on Data with Various Types
The tetrad constraint is a condition of which the satisfaction signals a rank reduction of a covariance submatrix and is used to design causal discovery algorithms that detects the existence of latent (unmeasured) variables, such as FOFC.
Unifying Causal Models with Trek Rules
In many scientific contexts, different investigators experiment with or observe different variables with data from a domain in which the distinct variable sets might well be related.
