Causal Model Analysis using Collider v-structure with Negative Percentage Mapping
This addresses a specific issue in causal inference for researchers, but appears incremental as it builds on existing methods like LiNGAM with a new scaling technique.
The paper tackled the problem of measuring information strength in causal inference by proposing a method using collider v-structures with Negative Percentage Mapping to set thresholds and direct edges in DAGs, resulting in a self-sufficient approach that detects latent confounders and causal directions, tested on simulated non-Gaussian datasets against DirectLiNGAM and ICA-LiNGAM.
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.