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The Population-based HIV Impact Assessment (PHIA) is an ongoing project thatconducts nationally representative HIV-focused surveys for measuring nationaland regional progress toward UNAIDS' 90-90-90 targets, the primary strategy toend the HIV epidemic. We believe the PHIA survey offers a unique opportunity tobetter understand the key factors that drive the HIV epidemics in the mostaffected countries in sub-Saharan Africa. In this article, we propose a novelcausal structural learning algorithm to discover important covariates andpotential causal pathways for 90-90-90 targets. Existing constrained-basedcausal structural learning algorithms are quite aggressive in edge removal. Theproposed algorithm preserves more information about important features andpotential causal pathways. It is applied to the Malawi PHIA (MPHIA) data setand leads to interesting results. For example, it discovers age and condomusage to be important for female HIV awareness; the number of sexual partnersto be important for male HIV awareness; and knowing the travel time to HIV carefacilities leads to a higher chance of being treated for both females andmales. We further compare and validate the proposed algorithm using BIC andusing Monte Carlo simulations, and show that the proposed algorithm achievesimprovement in true positive rates in important feature discovery over existingalgorithms.

Causal Structural Learning on MPHIA Individual Dataset