Causal Structural Learning on MPHIA Individual Dataset

TL;DR

This paper introduces a novel causal structural learning algorithm tailored for HIV survey data, which better preserves important features and pathways, leading to improved discovery of key factors influencing HIV awareness and treatment in sub-Saharan Africa.

Contribution

The paper presents a new causal structural learning algorithm that improves feature and pathway discovery over existing methods, validated on MPHIA data and simulations.

Findings

Age and condom use are key for female HIV awareness.

Number of sexual partners influences male HIV awareness.

Travel time to care facilities increases treatment likelihood.

Abstract

The Population-based HIV Impact Assessment (PHIA) is an ongoing project that conducts nationally representative HIV-focused surveys for measuring national and regional progress toward UNAIDS' 90-90-90 targets, the primary strategy to end the HIV epidemic. We believe the PHIA survey offers a unique opportunity to better understand the key factors that drive the HIV epidemics in the most affected countries in sub-Saharan Africa. In this article, we propose a novel causal structural learning algorithm to discover important covariates and potential causal pathways for 90-90-90 targets. Existing constrained-based causal structural learning algorithms are quite aggressive in edge removal. The proposed algorithm preserves more information about important features and potential causal pathways. It is applied to the Malawi PHIA (MPHIA) data set and leads to interesting results. For example, it discovers age and condom usage to be important for female HIV awareness; the number of sexual partners to be important for male HIV awareness; and knowing the travel time to HIV care facilities leads to a higher chance of being treated for both females and males. We further compare and validate the proposed algorithm using BIC and using Monte Carlo simulations, and show that the proposed algorithm achieves improvement in true positive rates in important feature discovery over existing algorithms.