Modified sharp regression discontinuity model to settings with fuzzy variables
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Updated: Jul 13, 2025
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Published on: July 3, 2020
Portia K Mafukidze1, Samuel M Mwalili2, Thomas Mageto2
1Department of Mathematics, The Pan African University, Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya. portiamafukidze@gmail.com.
Objective:
The goal of this study is to develop a Modified Sharp Regression Discontinuity model to predict alcohol consumption in People Living with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Previous studies focused on either fuzzy dependent or fuzzy independent variables separately. However, there is a gap in research that examines the interaction between both types of fuzzy variables thus the model considers both dependent and independent fuzzy variables.
Methods:
A statistical model was developed to predict the relationship between alcohol consumption and HIV progression. The model equations are solved numerically using parametric estimation.
Results:
In simulation studies, as the sample size expanded, the estimates derived from the modified sharp regression discontinuity model exhibited probabilistic convergence towards the true value, thereby validating the estimator of the Average Causal Effect's consistency. Counseling has an average causal effect in the sharp Regression Discontinuity Design (RDD) for compliers that is roughly equal to 0.199. This was the variation in Alcohol Use Detective Identification Test (AUDIT) threshold scores or the change in intercept scores when counseling was effective. Following six months of participation in the counseling program, AUDIT scores decreased, leading to an increase in Cluster of Differentiation 4 (CD4) counts and a decrease in viral loads.
Conclusion:
The Modified Sharp RDD offers a robust approach to handle fuzzy variables in causal inference. Our study contributes to the advancement of RDD methodology and its applicability in real-world settings with uncertain data.
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