Speaker |
Andrew Zhou Xiaohua |
|---|---|
Affiliation |
Peking University |
Date |
Friday, 22 November 2024 |
Time |
13:00-14:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/99748820264 |
Event series |
Jump Trading/ELLIS CSML Seminar Series |
Abstract |
Causal inference is an important tool for answering many scientific questions in medicine. However, many statistical and machine learning methods can only provide association answers. Research in medicine usually involves complex sceneries, and causal inference in complex sceneries becomes more complicated, particularly regarding to causal estimands and their identifiability. In this talk, I discuss how to make causal inference methods in medicine. First, I discuss causal inference methods for randomized trials with concurrent events. Randomized trials are often considered the gold standard design for evaluating causality for new interventions, for example, interventions aimed at improving service delivery, organization, quality, financing, and health care outcomes. In practice, however, many randomized protocol violations can occur for various reasons, and trials that do not fully adhere to protocol, concomitant problems such as truncation by death and rescue medication can occur. In such destructive randomized trials, standard estimation methods are no longer fully valid. In this talk, I discuss the new developments in causal inference in three violations of the ideal randomized scheme. Second, I discuss causal inference in precision medicine. Patients need to choose the best treatment plan according to their own conditions, and doctors need to choose the most suitable patient for personalized treatment according to the characteristics of each treatment plan. The selection of individualized treatment plans in precision medicine involves the intersection of multiple disciplines, such as medicine, probability and statistics, computational mathematics, and applied mathematics. Third, I discuss causal inference methods when the outcome of interest is latent and has to be estimated from data. Finally，I discuss causal inference for recommender systems. |
Biography |
Andrew is a Distinguished Chair Professor at Beijing International Center for Mathematical Research, and Chair of Department of Biostatistics at Peking University. |