  A major problem of causal inference is the arrangement of dependent nodes in
a directed acyclic graph (DAG) with path coefficients and observed confounders.
Path coefficients do not provide the units to measure the strength of
information flowing from one node to the other. Here we proposed the method of
causal structure learning using collider v-structures (CVS) with Negative
Percentage Mapping (NPM) to get selective thresholds of information strength,
to direct the edges and subjective confounders in a DAG. The NPM is used to
scale the strength of information passed through nodes in units of percentage
from interval from 0 to 1. The causal structures are constructed by bottom up
approach using path coefficients, causal directions and confounders, derived
implementing collider v-structure and NPM. The method is self-sufficient to
observe all the latent confounders present in the causal model and capable of
detecting every responsible causal direction. The results are tested for
simulated datasets of non-Gaussian distributions and compared with DirectLiNGAM
and ICA-LiNGAM to check efficiency of the proposed method.
