Stream 1: Field Experiment Methods & Causal Inference
Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun (2021) “A Prescriptive Analytics Framework for Optimal Policy Deployment using Heterogeneous Treatment Effects”, MIS Quarterly, 45(4), 1807-1832
Yicheng Song and Tianshu Sun “Ensembling Experiments to Optimize Customer Journey: A Reinforcement Learning Approach” (Adobe Faculty Research Award 2020, Marketing Science Institute (MSI) Research Grant 2021, CIST 2021), Minor Revision, Management Science
Matteo Sesia and Tianshu Sun, "Individualized Conditional Independence Testing under Model-X with Heterogeneous Samples and Interactions", Under Review, Journal of the American Statistical Association (JASA)
Xinze Du, Yingying Fan, Jinchi Lv, Tianshu Sun and Patrick Vossler (2021) “Dimension-Free Average Treatment Effect Inference with Deep Neural Networks”, Major Revision, Journal of Econometrics
James Enouen, Tianshu Sun and Yan Liu, "Measuring, Interpreting, and Correcting Algorithm Unfairness using Randomized Experiments", Working Paper
“IBASE: Adaptive Causal Inference by Integrating Big Data and Small Experiment”, with Jinchi Lv (Adobe Faculty Research Award 2017-2018)
Report on Digital Experimentation:
Ravi Bapna, Gordon Burtch, Yili Hong, Tianshu Sun, Jason Thatcher, "Lab experiment and field experiment in the digital age: Friend or foe?" Proceedings of the International Conference on Information Systems (ICIS 2018)