Skip to main content Skip to secondary navigation

Causal Science Conference 2022

Main content start

On November 14th, the Stanford Causal Science Center (SC²) held a one-day conference at the Koret-Taube Conference Center at the SIPER Building.

The program consisted of a day of presentations and posters by Stanford graduate students and postdoctoral scholars working on a breadth of causal science topics, from methodology to applications.  This event served as an opportunity for the community to learn more about the breadth of work happening at Stanford, to forge new connections, and ultimately to help shape a shared path for the future success of SC².


Welcome 9:00AM - Guido Imbens

Session 1 9:15-10:15 AM  
Ambarish Chattopadhyay Stanford Data Science Balanced and Robust Randomized Treatment Assignments: The Finite Selection Model for the Health Insurance Experiment and Beyond
Evan Munro GSB Economics Causal Inference in Equilibrium
Roshni Sahoo Computer Science Policy Learning with Competing Agents
Nick Gardner Classics, Computer Science Testing Compatibility of Causal Models With (Historical) Data Under Missingness Not at Random
Session 2 10:30 AM - 11:45 AM  
Qian Zhao Biomedical Data Science Selecting genetic variants using knockoffs under collider bias
Johannes Ferstad Management Science & Engineering Building, Evaluating, and Improving Remote Patient Monitoring
Felix Michalik Medicine Assessing the Real-Life Effect of the HPV Vaccine: A Regression Discontinuity Design Approach using Danish Electronic Health Records Data
Max-Emil King Political Science Bound by Borders: Voter Mobilization through Social Networks
Takuma Iwasaki Law School “None of My Business Anymore”: Prosecutors Bring Charges More Frequently When Another Prosecutor Handles the Subsequent Trial Process

Lunch: 11:45 AM - 1 PM

Session 3

1:00-2:00 PM  
Yujin Jeong Statistics Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty
Lea Bottmer Economics A Design-Based Perspective on Synthetic Control Methods 
Ying Jin Statistics Sensitivity analysis under the f-sensitivity models: a distributional robustness perspective
David Ritzwoller Graduate School of Business, Economics Probably Honest Confidence Intervals
Session 4 2:15-3:15 PM  
Kevin Han Statistics Detecting Interference in Sequential A/B Testing
Erin Craig Biomedical Data Science Finding and assessing treatment effect sweet spots in clinical trials
Scott Fleming Biomedical Data Science Evaluating Treatment Prioritization Rules with Rank-Weighted Average Treatment Effects
Jason Weitze Economics Attribution and Causality

Poster Session & Reception  3:15pm - 5:00pm