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FDS Statistics & Data Science Seminar
Dynamic causal inference under model misspecification
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Speaker: Jelena Bradic (UCSD) Professor of Statistics University of California, San Diego Monday, December 2, 2024 3:30PM - 5:00PM 3:30pm Pre-talk meet and greet teatime in 1307
4:00pm Talk Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.zoom.us/j/94223816617 |
Abstract: Estimating dynamic treatment effects is essential across various disciplines, offering nuanced insights into the time-dependent causal impact of interventions. However, this estimation presents challenges due to the “curse of dimensionality” and time-varying confounding, which can lead to biased estimates. Additionally, correctly specifying the growing number of treatment assignments and outcome models with multiple exposures seems overly complex. Given these challenges, the concept of double robustness, where model misspecification is permitted, is extremely valuable, yet unachieved in practical applications. This paper introduces a new approach by proposing novel, robust estimators for both treatment assignments and outcome models. We present a “sequential model double robust” solution, demonstrating that double robustness over multiple time points can be achieved when each time exposure is doubly robust. This approach improves the robustness and reliability of dynamic treatment effects estimation, addressing a significant gap in this field.
Speaker bio: Jelena Bradic received her Ph.D. in Operations Research and Financial Engineering from Princeton in Spring 2011 with a specialization in Statistics and Applied Probability under the direction of Jianqing Fan. Her research is in high dimensional statistics, stochastic optimization, asymptotic theory, robust statistics, functional genomics and biostatistics. She has received two teaching awards (for teaching assistants) at Princeton.
Website: https://www.math.ucsd.edu/people/profiles/jelena-bradic
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