Advancing Machine Learning through Causal Inference

Mon, Aug 5, 8:30 AM - 10:20 AM

Jean FengChair

Section on Statistical Learning and Data Science

4 Presentations

A hierarchical framework to explain performance differences of machine learning models across datasets

8:35 AM - 8:55 AM

Harvineet Singh (University of California, San Francisco)

Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift

8:55 AM - 9:15 AM

Hongxiang Qiu (Michigan State University)

Designing Adaptive Micro-randomized Trials for Mobile Health Studies with Zero-inflated Count Outcomes via Thompson Sampling

9:15 AM - 9:35 AM

Bibhas Chakraborty (Duke-NUS Medical School, National University of Singapore)

Co-authors: Xueqing Liu, Nina Deliu (MEMOTEF Department, Sapienza University of Rome, Rome, Italy), Tanujit Chakraborty (Sorbonne University Abu Dhabi), Lauren Bell (Medical Research Council Biostatistics Unit, University of Cambridge)

Robustness to Spurious Correlations: Lessons Learned on Interpolation and Causal Thinking

9:35 AM - 9:55 AM

Yoav Wald (New York University)