Causality and Machine Learning/AI workshop

Thank you to everyone who joined us for this workshop. It was a fantastic turnout. A special thanks to our presenters and sponsors. You may view the event photos here.

I welcome any feedback or other engagement from the presenters, sponsors, and participants.

Le Wang, Professor and David M. Kohl Chair

WORKSHOP | ONE TIME EVENT
Scholars from renowned universities such as Cornell, University of Chicago, and UC Irvine, along with various departments at Virginia Tech, are coming together to explore the critical intersections of causality and machine learning/AI.

Causality is pivotal in social science, business, and applied economics, shaping the future of machine learning and AI applications in diverse industries. This fundamental concept has earned prestigious recognition, with the 2021 Nobel Prize in Economics and the 2012 Turing Award in Computer Science honoring contributions to causality.

The upcoming workshop aims to foster collaborations that transcend traditional boundaries, drive research forward, and align closely with Virginia Tech's cutting-edge work.

This workshop is in partnership with and sponsored by:

The details

Pre-workshop: Tutorial session on causal machine learning by Hengrui Cai, author of Causal Decision Making

  • Date:  Friday, November 1, 2024
  • Time: 2:00 - 5:00 pm
  • Location: Room 132 in the Data and Decision Sciences Building

Workshop:

  • Date: Saturday, November 2, 2024
  • Time: 8:30 am - 5:00 pm
  • Location: The Inn at Virginia Tech and Skelton Conference Center; Solitude Room
AGENDA

8:30 - 9:00 am | Meet and greet over coffee and snacks

9:00 - 9:15 am | Welcome

  • Dan Sui
    Senior Vice President and Chief Research and Innovation Officer
  • George Davis
    Interim Department Head, Department of Agricultural and Applied Economics
  • Le Wang
    Director of the Kohl Centre and David M. Kohl Chair and Professor in the Department of Agricultural and Applied Economics

9:15 - 10:30 am | Session 1

  • Lydia Manikonda
    Title: Causality in the Age of Generative AI
  • Hengrui Cai
    Title: Towards Trustworthy Machine Learning: A Causal Lens on Learning Non-Spuriousness
  • Jacek Kibilda
    Title: Current and Prospective Uses of AI/ML in Addressing Problems Related to 6G Security

10:30 - 11:00 am | Coffee and networking break

11:00 am - 12:15 pm | Session 2

  • Rui Fan
    Title: Rehabilitating the Once-Abandoned Endogenous IV
  • Zhenshan Chen
    Title: Causal Machine Learning for Policy Evaluation with Housing Transaction Data
  • Kyle Butts
    Title: Dynamic Treatment Effect Estimation with Interactive Fixed Effects and Short Panels

12:15 - 1:30 pm | Lunch - Smithfield Room

If you are interested in one of the available lunch spots (limited to 30 participants), please email Jennifer Shelton. Spots will be allocated on a first-come, first-served basis.

1:30 - 3:10 pm | Session 3

  • Wenzhuo Zhou
    Title: Bi-Level Offline Reinforcement Learning
  • Klaus Moeltner
    Title: Controlling for problematic responses in survey data: A Causal Forest approach
  • Bo Zhou
    Title: Valid Post-Inference for Contextual Bandit Problems
  • Xinran Li
    Title: Randomization inference and sensitivity analysis for quantiles of individual treatment effects

3:10 - 3:30 pm | Coffee and networking break

3:30 - 5:00 pm | Session 4

  • Louise Laage
    Title: Estimating Stochastic Block Models in the Presence of Covariates
  • Yan Xu
    Title: Heterogeneous Complementarity and Team Design
  • Christina Yu
    Title: Causal Inference in the Presence of Network Interference with Low-Order Interactions

Organizing Committee: Le Wang (AAEC), Xin Xing (Statistics), and Bo Ji (Computer Science)