Lencer Ogutu tackles counterfactual estimation

Friday, Oct 18, 2019
by Sharon Adarlo

Lencer OgutuLencer Ogutu, 22, Class of 2020


Ogutu is pursuing a bachelor’s degree at the Operations Research and Financial Engineering (ORFE) department, while taking courses to fulfill the Undergraduate Certificate in Statistics and Machine Learning (SML), awarded by the Center for Statistics and Machine Learning (CSML).


Ogutu’s independent project for her CSML certificate (also her thesis project for her ORFE degree) focuses broadly on causal inference and specifically to counterfactual estimation, a type of statistical analysis that models what would happen in a situation if an intervention had not been applied. Her project will involve the use of machine learning tools and other data science techniques. 

“For example, there may be a feature that a company wants to roll out, but managementwants to predict its impact on product engagement before implementing it,” Ogutu said. “They can look at previous engagement levels and market reception to other feature introductions and see how that went. They can also carry out pilots in some regions randomly and perform an analysis that compares user engagement in regions with the pilots, and those without, where the regions without the pilots provide counterfactual information.”

For her project, Ogutu plans on using data from a solar energy company based in her hometown, Nairobi, Kenya. Ogutu will be using the company’s sales data to analyze the impact of adding a new product, such as a solar-powered torch, to different sales regions. Introducing a new product would be considered a form of intervention in her counterfactual analysis, Ogutu said. It would be an opportunity to test if Ogutu’s ensemble model for counterfactual estimation will be more accurate than older models, and it would also be an opportunity to see how clients are using the product. 

Ogutu was an electrical engineering major when she arrived at Princeton, but she became interested in data science and enrolling in the CSML certificate program after taking POL 245, a class on data visualization and statistics. She found the concepts being taught on data intriguing.

Ogutu said the CSML certificate courses have proved to be helpful in her tackling of more complex concepts in ORFE classes. 

“They have really helped me in terms of giving me a strong foundation in statistics and data science,” she said.

In the future, Ogutu said she wants to work for a few years as a data scientist or data analyst either in social research or the financial services industry. Then she plans on applying to graduate school, either for a master’s or doctoral degree.

Extracurricular activities:

She is the co-president of the National Society of Black Engineers and one of the founders for the Society of African Internationals at Princeton. She is also a member of the Africa Summit at Princeton Planning Committee, Society for Women Engineers, Princeton Women in Computer Science and Princeton Data Club.

For fun:

Ogutu enjoys hanging out with friends and having political debates on the future Africa and how to bring more institutional recognition to African issues. She also loves cooking and trying out new restaurants.