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Found 3 papers, 1 papers with code
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
Many real datasets contain values missing not at random (MNAR).
Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values
The PC algorithm allows investigators to estimate a complete partially directed acyclic graph (CPDAG) from a finite dataset, but few groups have investigated strategies for estimating and controlling the false discovery rate (FDR) of the edges in the CPDAG.
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models
The Trek Separation Theorem (Sullivant et al. 2010) states necessary and sufficient conditions for a linear directed acyclic graphical model to entail for all possible values of its linear coefficients that the rank of various sub-matrices of the covariance matrix is less than or equal to n, for any given n. In this paper, I extend the Trek Separation Theorem in two ways: I prove that the same necessary and sufficient conditions apply even when the generating model is partially non-linear and contains some cycles.
