Copula-Based Estimation of Causal Effects in Multiple Linear and Path Analysis Models

TL;DR

This paper explores a copula-based method for estimating causal effects in path analysis models, comparing it with classical approaches through simulations and real data applications.

Contribution

It introduces a copula-based framework for path analysis, allowing for flexible dependence modeling between variables beyond traditional methods.

Findings

Copula-based approach improves estimation accuracy in complex dependence scenarios

Classical OLS performs well in simple linear settings but less so with complex dependencies

Real data applications demonstrate the practical utility of the copula method

Abstract

Regression analysis is one of the most popularly used statistical technique which only measures the direct effect of independent variables on dependent variable. Path analysis looks for both direct and indirect effects of independent variables and may overcome several hurdles allied with regression models. It utilizes one or more structural regression equations in the model which are used to estimate the unknown parameters. The aim of this work is to study the path analysis models when the endogenous (dependent) variable and exogenous (independent) variables are linked through the elliptical copulas. Using well-organized numerical schemes, we investigate the performance of path models when direct and indirect effects are estimated applying classical ordinary least squares and copula-based regression approaches in different scenarios. Finally, two real data applications are also presented to demonstrate the performance of path analysis using copula approach.