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Workshop: Experimentation and Causal Inference in the Tech Sector

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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. 

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.