2024 Causal Science Center Conference
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Join the Stanford Causal Science Center (SC²) for a one-day conference on Friday, October 11th, 2024! This event is open to the Stanford Community and Stanford Data Science Affiliates*.
The program consists of presentations and posters by Stanford graduate students and postdoctoral scholars covering topics, such as methodology, applications, and more. This convening offers an opportunity for the community to learn more about the breadth of work, forge new connections, and help shape a path for the future success of SC².
*If you are not affiliated with Stanford and your organization is not yet a Stanford Data Science Affiliate Program member, please visit our Affiliate Program page.
Agenda
Time | Session Name | Speaker(s) |
| 8:30-9:00 AM | Registration & Breakfast | |
| 9:00-9:15 AM | Welcome | Guido Imbens |
| 9:15-10:15 AM | Session 1 — Chair: Lihua Lei | |
| When Does Interference Matter? Decision-Making in Platform Experiments | Anushka Murthy | |
| Optimal Mechanisms for Demand Response: An Indifference Set Approach | Mohammad Mehrabi | |
| Regression Adjustments for Experimental Designs in Two-Sided Marketplaces | Timothy Sudijono | |
| Treatment Effect Estimation Under Network Interference Using Data-driven State Evolution | Sadegh Shirani | |
| 10:15-10:35 AM | Break | |
| 10:35-11:35 AM | Session 2 — Chair: Yuyan Wang | |
| Difference-in-differences using longitudinal wastewater SARS-CoV-2 concentrations to evaluate the impact of Stanford’s COVID-19 public health policies | Elana Chan | |
| Real-World Causal Inference in Oncology: Regression Discontinuity Designs to Improve Skin Cancer Care | Max Schuessler | |
| Isolated Effects of Surgeons Versus Hospitals on Outcomes of Cardiovascular Operations | Yan Mia Min | |
| Asymptotic Bias-Aware Inference in Regression Discontinuity Designs under Higher-Order Smoothness | Aditya Ghosh | |
| 11:35 AM-12:35 PM | Lunch | |
| 12:35-1:35 PM | Session 3 — Chair: Vasilis Syrgkanis | |
| Root cause discovery | Jinzhou Li | |
| Using extreme events for causal inference: Causality in extremes of time series and extrapolation of causal effects | Juraj Bodik | |
| Dynamic Local Average Treatment Effects | Ravi Sojitra | |
| Price Experimentation and Interference | Orrie Page | |
| 1:35-1:55 PM | Break | |
| 1:55-2:55 PM | Session 4 — Chair: Emma Brunskill | |
| Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification | Chao Qin | |
| Efficient combination of observational and experimental datasets under general restrictions on outcome mean functions | Harrison Li | |
| Regularized DeepIV with Model Selection | Hui Lan | |
| Design-based inference for generalized network experiments with stochastic interventions | Ambarish Chattopadhyay | |
| 2:55-3:00 PM | Closing | |
| 3:00-5:00 PM | Poster Session and Reception |