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This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
This one-day event provides a unique opportunity to engage with top experts in experimentation and causal inference from both academia and industry.
The goal of this workshop is to bring together researchers, practitioners, and industry professionals to discuss cutting-edge methodologies and their real-world applications. We are thrilled to share that we have an excellent lineup of speakers who are leading figures in the tech industry and academia. This workshop is an excellent opportunity for networking, learning, and discussing the latest trends in causal inference in the tech sector.
| Agenda: | |
| 9:30-10:00am | Registration |
| 10:00 - 10.15am | Opening: Guido Imbens |
| 10.15-11.45am | Session 1: Chaired by Emma Brunskill |
| Martin Tingley (Netflix), Experimentation Platform at Netflix: Building Useful Inference | |
| Min Liu (LinkedIn), Online Experimentation at LinkedIn | |
| Art Owen (Stanford), Multibrand Geographic Experiments (with Tristan Launay) | |
| 11:45-12:00pm | Break |
| 12:00 - 12:45pm | Poster Session |
| 12:45-1:30pm | Lunch |
| 1:30-3:00pm | Session 2: Chaired by Stefan Wager |
| Emily Glassberg-Sands (Stripe), Policy Optimization at Stripe (with Kyle Carlson) | |
| Alex Chin (Lyft), Policy Evaluation and Optimization with Multi-agent RL Environments at Lyft | |
| Bin Yu (UC Berkeley), Using Predictability and Stability to Reduce Design Space for Causality | |
| 3:00-3:15pm | Break |
| 3:15-4:45pm | Session 3: Chaired by Ramesh Johari |
| Ali Rauh (Airbnb), Experimentation Challenges at Airbnb | |
| Ramon Huerta (Amazon), Mitigating the impact of confounders in Machine Learning | |
| Vasilis Syrgkanis (Stanford), Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments | |
| 4:45-6:00pm | Reception |
SC^2 focuses on providing an interdisciplinary community for scholars interested in causality and causal inference. We aim to be a nexus where participants can learn about methods for causal inference in other disciplines and find opportunities to work together on such questions.
This event is sponsored by the Stanford Causal Science Center (SC²) and Stanford Data Science.
