My research focuses on developing statistical methods and frameworks that help Marketers, Clinicians, and Policy-makers better conduct Randomized Controlled Trials (A/B testing) and also optimally target subjects and treatments in a budget-constrained, heterogeneous treatment effect environment. These questions fall in the line of literature related to Heterogeneous Treatment effects and Multi-Armed Bandits. I use a mix of Statistics, Machine Learning, and Causal Inference to solve the above problems.
- A Prescriptive Analytics Framework for Optimal Deployment using Heterogeneous Treatment effects - Management Information Systems Quarterly
With Edward Mcfowland III, Ravi Bapna, Tianshu Sun
- Trading off regret and rewards in Multi-Armed Bandits
With Edward Mcfowland III, Ravi Bapna
- Can Social Referrals Outperform Algorithmic Targeting
With Ravi Bapna, Colleen Manchester, Gautam Ray, Edward Mcfowland III
- The effect of emerging online new media on traditional media consumption. the case of eSports and Traditional Sports