[3] Sekhon's primary research interests lie in causal inference, machine learning, and their intersection.
[4] He has also published research on their application in various fields including voting behavior, online experimentation, epidemiology, and medicine.
[2] Sekhon has authored or co-authored dozens of journal articles and several widely used software packages.
The topics of his scholarship include experimental research methods, machine learning for estimating causal effects, election fraud, and matching.
[10] As of 2021, his research focuses on developing interpretable and credible machine learning methods for estimating causal relationships.