Workshop: Experimentation and Causal Inference in the Tech Sector
This one-day event will be held on June 5, 2023, at Vidalakis Hall on Stanford Campus, providing 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.
Vidalakis Dining Hall (Meeting Room)
Schwab Residential Center, Stanford University
680 Jane Stanford Way, Stanford, CA 94305
Arrival and Parking:
- If you arrive via ride service, with the address above, they will drop you off just outside the GSB campus, and you can then use the walking map.
- If you drive to campus, parking is available at Knight Management Center Garage. Visitor Parking is paid via the park mobile app. Please anticipate a 10-minute walk from the parking lot to the meeting room.
|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)|
|12:00 - 12:45pm||Poster Session|
|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|
|3:15-4:45pm||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|
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.