In social science research, we often focus on causal issues. For example, does a patient recover because a certain drug is effective? However, in the case of multiple influencing factors, how to correctly estimate the causal effect of a drug on a disease, that is, the conditional marginal effect of a drug, has always been a difficulty in research. At a computational social science workshop held recently by the Faculty of Social Sciences at UM, Professor Weiwen Yin from the Department of Government and Public Administration introduced his research results, which include a robust prediction method for conditional marginal effects. This method may provide a solution to the above difficulties with some new ideas.
Professor Tianji Cai, associate dean of the Faculty of Social Sciences at UM, said in his speech that the Computational Social Sciences Workshop has been very popular since its inception, attracting many teachers and students every time. Therefore, workshops will continue to be held in the new academic year, hoping to promote interdisciplinary academic exchanges through this platform. Professor Cai pointed out that causal inference in social science research is a very important theoretical issue, and the estimation of conditional marginal effects is the basis of this issue. He hopes that everyone can actively discuss this topic.
