Mentors
Anjana Susurla, Department of Accounting and Information Systems, Michigan State University (Postdoc supervisor)
Rema Padman, Heinz College of Information Systems & Public Policy, Carnegie Mellon University (Postdoc supervisor)
Xiao Liu, Department of Information Systems, Arizona State University (Postdoc supervisor)
Ruben Juarez, Department of Economics, University of Hawaii at Manoa (PhD advisor)
Farzana Nasrin, Department of Mathematics, University of Hawaii at Manoa (Master advisor)
Barbara DeBaryshe, Center on the Family, University of Hawaii at Manoa (RA supervisor)
Shirley Daniel, Shidler College of Business, University of Hawaii at Manoa (RA supervisor)
Yuanzhang Xiao, College of Engineering, University of Hawaii at Manoa (RA supervisor)
Resources
ML for economists and any other researchers (Dario Sansone)
ML and causal inference: A short course (Athey and Wager)
Causal ML Book (free book)
Causal ML libraries/packages: DoWhy, EconML, and CausalML(for causal effect estimations), DECI (for causal discovery), DiCE (for diverse counterfactual explanations). Some other libraries include CausalNex, CausalPy (causal inference in quasi-experimental settings), DeepIV (for counterfactual prediction), and DoubleML (double/debiased ML).
Microsoft's causal tools (2021)
EconML/CausalML tutorial (KDD 2021)
Machine learning for healthcare (MIT-OCW, Spring 2019)
FAIR data principles (for data-driven work)
Integrated Inferences (free book)
Causal mediation analysis (Van der Weele)
Introduction to TDA (2024)
Causal inference and machine learning's scholars/labs
Judea Pearl (UCLA)
Bernhard Schölkopf (Max Planck Institute)
Elias Bareinboim (Columbia)
Kosuke Imai (Harvard)
Susan Athey (Stanford)
Amit Sharma (MSR India)
Chenhao Tan (Chicago)
Kun Zhang (CMU)
van der Schaar lab (Cambridge)
Seminars
Applied ML, Economics and DS (AMLEDS) https://sites.google.com/view/amleds/home
Professional associations/societies
Viet AI & Business Academic Network (Viet-AI-Bus)