Judith Lok is a tenured associate professor of mathematics and statistics at Boston University. Her research focuses on causal inference methods and their applications, including HIV/AIDS, bacterial infections, and maternal-and-child health. For example, in causal mediation analysis, she has introduced “organic” direct and indirect effects, an intervention-based approach that eliminates the need to be able to “set” mediators to specific values. Moreover, she has developed Learn-As-you-GO (LAGO) clinical trial designs, wherein the intervention package composition changes as earlier-stage outcomes become available. She has also contributed to other causal inference methods, including inverse probability of censoring/treatment weighting and structural nested models.

In this lecture, Lok will speak about her project to write “Causal Inference: A Statistics Playground,” a textbook designed for students and statisticians within and outside academia who work or intend to work in causal inference. Causal inference methods seek to address questions like “what would happen if” through data analysis. This textbook will primarily concentrate on non-randomized data, which are abundant. Estimating treatment effects from non-randomized data is challenging due to confounding by indication: when comparing treated and untreated individuals/units, differences arise not only from the treatment but also from pretreatment differences between the treated and untreated groups. Causal inference offers methods to overcome confounding by indication and other biases, allowing for the estimation of treatment effects from non-randomized data.

Judith Lok holds a PhD in mathematical statistics from the Vrije Universiteit Amsterdam. Before joining Boston University, she was a faculty member in Harvard University’s Department of Biostatistics for 13 years.