Causal Structural Learning on MPHIA Individual Dataset

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 constraint-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) dataset 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. Supplementary materials for this article are available online.

KEYWORD:

• 90-90-90 targets
• Causal structural learning
• HIV
• PHIA

Supplemantary Materials

The supplementary materials contain the proofs of Propositions 1 and 2. The supplementary materials also present additional details for the six Tri90 datasets. The supplementary materials also present some analysis results of the MPHIA data, more simulation studies, as well as the code to carry out the analysis in the paper. A summary for the notations in the proposed algorithms and parts of MPHIA codebook are also provided in the supplementary materials.

Acknowledgments

The authors are grateful to the editor, AE and reviewers for their constructive comments, which lead to a significant improvement of this work. We also appreciate the PHIA teams making the data publicly available for research purposes.

Funding

This work was supported by an NIH grant R01AI136664 and an NSF grant DMS 1820702, 1953196 and 2015539. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NSF or the NIH.