30802 - Machine Learning and Classical Statistical Approaches: Trade-offs, Integration, and Debate (Co-sponsored by Section on Mathematical Sociology)

Description

Machine Learning is a research approach that is both inductive and deductive and 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. When the explicit model structure is unclear and algorithms with a good performance are difficult to attain, machine learning builds models and algorithms by learning and improving from data. This section welcomes the following research that: 1) demonstrates the implications of this new paradigm to data, methods, and theory development, or 2) compares machine learning with the classical approach of parameter estimation regressions, or 3) incorporates predictive modeling to produce improved models that combine explanation and prediction.

Sub Unit

• Section on Methodology / Machine Learning and Classical Statistical Approaches: Trade-offs, Integration, and Debate (Co-sponsored by Section on Mathematical Sociology)

Presider

• Xiaoling Shu, University of California-Davis

Individual Presentations

• Couples’ Household Labor: New Insights with Observed-Synthetic Data Using Supervised Machine Learning and Actor-Partner Interdependence Model - Xingyun Wu, Johns Hopkins University

• Algorithmic Tradeoffs, Applied NLP, and the State-of-the-Art Fallacy - AJ Alvero, Cornell University; Ruohong Dong, University of Arizona; Klint Kanopka, New York University; David Lang, Stanford University

• How Accurately Can Machine Learning Algorithms Predict a Person’s Future? - Emily M. Cantrell, Princeton University; Pranay Anchuri; Hanzhang Ren, Stanford University; Matthew J. Salganik, Princeton University

• Discovering Connections Between Networks and Outcomes: Labeled Subgraph Kernels for Social Network Analysis - Carter T. Butts, University of California-Irvine

Session Organizer

• Xiaoling Shu, University of California-Davis