Causal Model Analysis using Collider v-structure with Negative Percentage Mapping

TL;DR

This paper introduces a novel causal inference method using collider v-structures combined with Negative Percentage Mapping to accurately determine causal directions and confounders in DAGs, even with latent variables.

Contribution

The proposed method uniquely employs NPM to scale information flow and effectively detect causal directions and confounders, outperforming existing algorithms like DirectLiNGAM and ICA-LiNGAM.

Findings

Successfully detects all latent confounders in simulated data

Accurately determines causal directions in non-Gaussian distributions

Outperforms existing causal inference methods in efficiency

Abstract

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.