Sharif Mahmood
Assistant Professor
MCS 204
(501) 450-5658
Research Interest:
Causal inference, Machine learning, and the Design of Experiments
My primary focus is on developing novel machine learning algorithms and statistical methodologies to facilitate causal inference in observational data, with a specific emphasis on the design of experiments to improve causal identification. I am interested in exploring how machine learning techniques can be leveraged to address the fundamental problems of confounding, selection bias, and unmeasured variables when drawing causal inferences from non-experimental data. Additionally, I aim to advance the state of the art in experimental design, with a particular focus on optimizing the allocation of treatments and interventions in controlled experiments to maximize the quality and generalizability of causal conclusions. Ultimately, my research goal is to contribute to the development of more robust, interpretable, and actionable approaches for estimating causal effects from data, bridging the gap between observational and experimental studies, and promoting the responsible use of machine learning in solving real-world problems while maintaining a strong grounding in causal reasoning.