This (growing) collection of essays about causal inference and experimental design is being written by Guillaume Basse and Iav Bojinov. It is our attempt at explaining some ideas from our work — and more broadly, from our field — in an accessible way.
I am an Assistant Professor in the MS&E and Statistics departments at Stanford. My research focuses on Causal Inference and Design of Experiments in the presence of interference. I got my PhD in Statistics at Harvard in 2018, under the supervision of Edo Airoldi, then spent a year as a postdoctoral fellow in the Statistics Department at UC Berkeley where I was advised by Peng Ding. Before coming to the US I attended the Ecole Centrale Paris, where I studied Applied Mathematics and Engineering. I have lived in France, Israel, the US and Senegal, where I was born. My personal website is here.
I am an assistant professor of business administration in the Technology and Operations Management unit at Harvard Business School and a faculty affiliate in the Department of Statistics at Harvard University. My research interest is at the interface of causal inference, experimental design, and large-scale computing with the overall goal of democratizing statistical methods in order to help firms innovate and grow. Currently, I am actively pursuing three related research areas: design and analysis of experiments in complex settings, demystifying the value and limitation of experimentation, and understanding the role of data science in the modern AI organization. My personal website is here.