Knowledge Discovery in the Social Sciences: Machine Learning and Causal Discovery

Banner_Shanghai New York Urban Research Forum_Professor Xiaoling Shu


Knowledge discovery and data mining emerged from a necessity of big data requiring new analytical methods beyond the traditional statistical approaches to discover new knowledge from the data mine. This emergent approach is a dialectic research process that is both deductive and inductive. The data mining approach automatically or semi-automatically considers a larger number of joint, interactive, and independent predictors to address causal heterogeneity and improve prediction. Instead of challenging the conventional model-building approach, it plays an important complementary role in improving model goodness of fit, revealing valid and significant hidden patterns in data, identifying nonlinear and non-additive effects, providing insights into data developments, methods, and theory, and enriching scientific discovery. Machine learning builds models and algorithms by learning and improving from data when the explicit model structure is unclear and algorithms with good performance are difficult to attain. The most recent development is to incorporate this new paradigm of predictive modeling with the classical approach of parameter estimation regressions to produce improved models that combine explanation and prediction.


Xiaoling Shu is professor of sociology at the University of California Davis. She holds a M.S. in Computer Science and Ph.D. in Sociology, University of Minnesota. Her research focuses on the impacts of two of the most profound processes of our times – marketization and globalization – on gender inequalities, subjective sense of well-being, and gender, family, marriage, and sexual behaviors and attitudes. She uses data science models on national and international data to carry out country-specific (China and the United States) and cross-national analyses. She has published in Social Forces, Social Science Research, Sociology of Education, Research in Social Stratification and Mobility, Journal of Family Issues, and Social Science Quarterly. She is the author of Knowledge Discovery in the Social Science: A Data Mining Approach. Her second book, Chinese Gender, Marriage, and Family In Transition: Confucianism, Socialism, and Modernization, has been submitted for production. She served as Chair of the Section on Asia and Asian America of the American Sociological Association in 2018-21, President of the International Chinese Sociological Association (formerly NACSA) in 2016-17, director of East Asia Studies in 2017-22, and Vice-Chair and Graduate Director of Sociology at UC Davis in 2014-17.

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