The Boston Chapter of the American Statistical Association is delighted to announce that Dr. Judith Lok, Professor in the Department of Mathematics and Statistics at Boston University, is the esteemed recipient of the 2025 Mosteller Statistician of the Year Award.
Honoree and Speaker: Dr. Judith Lok, Professor in the Department of Mathematics and Statistics at Boston University
Talk Title: Causal Inference: a Statistics Playground, with Lessons Learned
Date: April 28, 6:00 - 8:00 PM (ET)
- 6 - 6:40 pm (approximately): social and reception-style dining
- 6:40 - 8 pm (approximately): award presentation.
If you would like to join us for the award presentation part only, you may choose the "Lecture only, no dinner" ticket category
Location: River Room of the Boston University Hillel, 213 Bay State Road, MA 02215
*Reception style dinner will be served.
Parking: parking is available at Warren Towers Garage (700 Commonwealth Ave, Boston, MA 02215, with the entrance on Hinsdale Mall). It is about a 3-minute walk from the reception venue. You will be able to pull a ticket at the gate and then pay with a credit card as you leave.
Registration
Abstract:
I am writing a textbook titled "Causal Inference: A Statistics Playground". It targets students and statisticians in and out of academia who work with or want to learn about causal inference.
Causal inference methods address questions like "what would happen if" through data analysis. The textbook primarily concentrates on data from non-randomized (observational) studies, which are abundant. Estimating treatment effects from observational data is challenging due to confounding by indication: when comparing treated and untreated individuals/units, differences arise not only from the effect of the treatment but also from pretreatment differences between the two groups. Causal inference offers methods to overcome confounding by indication and other biases, allowing for the estimation of treatment effects from observational data.
In this lecture, I will explore recent applications of causal inference, touch upon the methods behind them, and share key lessons learned. Scientific progress does not come from following the crowd. When I began studying causal inference, many scientists dismissed the notion of drawing causal conclusions from non-randomized data. More recently, skepticism among statisticians about analyzing data of trials where the intervention package changed over time led me to work on Learn-As-you-GO (LAGO). LAGO is an adaptive trial design that adjusts the composition of a multi-component intervention package during the trial. Our team has developed conditions on the learning process that adjust the intervention package to prevent failed trials while obtaining valid inference (consistency, asymptotic normality, and preservation of Type-1 error). Thus, statistics in general, and causal inference in particular, is a playground where it is okay to break the rules if one is prepared to suffer (or enjoy) the consequences: developing rigorous mathematical proofs. Another lesson learned: it is rarely feasible to carry out reliable causal inference applications in isolation. Engaging with subject matter experts about assumptions and models is both a pleasure and a necessity for drawing valid causal conclusions. As a result, many causal inference statisticians become data scientists: they are skilled in both statistics and a field of application. For me, the field of application has been HIV/AIDS and, more broadly, public health.
Bio of the Speaker:
Dr. Lok is a distinguished leader in the field of causal inference, renowned for her pioneering contributions to mediation analysis, structural nested models, and adaptive causal methods. Her research has significantly advanced statistical methodology and its applications in clinical and biomedical research, particularly in improving treatment strategies for infectious diseases. She has published extensively in leading journals, including Annals of Statistics, Statistics in Medicine, Biometrics, and high-impact clinical journals.
Dr. Lok has also been the principal investigator on numerous prestigious grants, including an NIH R01 award for developing methods to analyze the causal effects of HAART on HIV outcomes, an NSF grant to advance causal inference methods for mediation and methods to compare confidence regions, and an mPI R01 to develop Learn-As-you-GO (LAGO) adaptive clinical trials.
Beyond her research, Dr. Lok is a dedicated mentor and a passionate advocate for student development. Since 2019, she has served as the faculty advisor for the Boston University Student Chapter of the ASA (BUSCASA), where she has played an instrumental role in fostering student engagement in the statistical community. Her leadership has been particularly evident in the New England Student Research Symposium on Statistics and Data Science, which she co-organized in 2020, 2022, and 2024. Dr. Lok has consistently gone above and beyond to mentor students, providing personalized feedback on their presentations and ensuring they receive meaningful learning experiences.
Her service to the Boston Chapter and the broader statistical community is exceptional. Dr. Lok was instrumental in organizing the first in-person Student Research Symposium at Boston University, securing funding and suitable venues to create an enriching experience for participants. Her contributions extend nationally, having served as IMS chair for the 2017 ENAR Spring Meeting, organizing multiple invited sessions at Joint Statistical Meetings, and contributing as associate editor of Epidemiological Methods and co-editor of Statistical Communications in Infectious Diseases.
About the Award: Each year, the Boston Chapter presents the Mosteller Statistician of the Year Award to a distinguished statistician who has made exceptional contributions to the field and has demonstrated outstanding service to the statistical community, including the Boston Chapter. Originally established in 1990 as the Statistician of the Year Award, it was renamed in 1997 in honor of its first recipient, Fred Mosteller, on his 80th birthday.
For more information on the award and past recipients, please visit the Boston Chapter's Mosteller Statistician of the Year Award page.
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Jianchang Lin
President, Boston Chapter of the American Statistical Association (BCASA)
Executive Director, Statistics
Takeda
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