Machine Learning for Phone-Based Relationship Estimation: The Need to Consider Population Heterogeneity. - Tony Liu 004; Jennifer Nicholas; Max-Marcel Theilig; Sharath Chandra Guntuku; Konrad P. Körding; David C. Mohr; Lyle H. Ungar (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2019)

Quantifying causality in data science with quasi-experiments. - Tony Liu 004; Lyle H. Ungar; Konrad P. Kording (Nat. Comput. Sci., 2021)

Data-driven exclusion criteria for instrumental variable studies. - Tony Liu 004; Patrick N. Lawlor; Lyle H. Ungar; Konrad P. Kording (CLeaR, 2022)

A large language model-assisted education tool to provide feedback on open-ended responses. - Jordan K. Matelsky; Felipe Parodi; Tony Liu 004; Richard D. Lange; Konrad P. Kording (CoRR, 2023)

Automated Detection of Causal Inference Opportunities: Regression Discontinuity Subgroup Discovery. - Tony Liu 004; Patrick N. Lawlor; Lyle H. Ungar; Konrad P. Kording; Rahul Ladhania (Trans. Mach. Learn. Res., 2023)

Measuring Causal Effects of Civil Communication without Randomization. - Tony Liu 004; Lyle H. Ungar; Konrad P. Kording; Morgan McGuire (ICWSM, 2024)

Computational kinematics of dance: distinguishing hip hop genres. - Ben Baker; Tony Liu 004; Jordan Matelsky; Felipe Parodi; Brett Mensh; John W. Krakauer; Konrad P. Kording (Frontiers Robotics AI, 2024)