eventConference
Tim de Silva | AI Financial Advice: Supply, Demand, and Life Cycle Implications
SeminarOct 07, 202612:00 PM - 1:15 PM
October
07
2026
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This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Join us for a one-day conference hosted by the Stanford Causal Science Center (SC²), bringing together our community to explore the breadth of work in causal inference, spark new connections, and help chart the path forward.
The program will feature graduate students and postdocs presenting both cutting-edge methods and real-world applications through a dynamic mix of 15-minute talks and a poster session.
| 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: Guido Imbens | |
| Causal Inference in Dynamic Thresholding Designs | Aditya Ghosh | |
| Active Learning for Stochastic Contextual Linear Bandits | Ishani Karmarkar | |
| Shortlist Experiments | Chao Qin | |
| Causal Selection Decisions: An Almost SURE Approach | Timothy Sudijono | |
| 10:15-10:35 AM | Break | |
| 10:35-11:35 AM | Session 2 — Chair: Stefan Wager | |
| Synthetic IV Estimation in Panels | Jaume Vives | |
| Estimating Variances for Causal Panel Data Estimators | Alexia Olaizola | |
| Weight and See: A Normative Approach to Convex Weighting Estimators | Sarah Vicol | |
| Learning Treatment Representations for Downstream Instrumental Variable Regression | Shiangyi Lin | |
| 11:35 AM-12:35 PM | Lunch | |
| 12:35-1:35 PM | Session 3 — Chair: Steven Goodman | |
| Quasi-Experimental Evaluation of Treatment for Non-small Cell Lung Cancer: Real-World Evidence from the US | Max Schuessler | |
| Causal Examination of Temporal and Semantic Factors in Conspiracy Sharing on X | Fabian Tschofenig | |
| Evaluating Algorithm-Assisted Decision-Making: Estimands and Experimental Designs in the Presence of Exposure-Time Dependent Treatment Effects | Maggie Wang | |
| Germline mtDNA Variants as Instruments for Causal Inference in Tumor Mitochondrial Genomics | Mengran Zhang | |
| 1:35-1:55 PM | Break | |
| 1:55-2:55 PM | Session 4 — Chair: Ramesh Johari | |
| Reconstructing Innovation Counterfactuals: Synthetic Control and SDID in the Microsoft Antitrust Case | Cuehyon Kim | |
| Estimation under Nonstationarity via the Truncated Policy Gradient Estimator | Wenqian Xing | |
| Self-Preferencing as a Fairness Provision: A General Definition | Andreas Haupt | |
| Many Hands Make Light Work: Long-Term Effect Inference for the Experiment Reservoir | Will Hartog | |
| 2:55-3:00 PM | Closing Remarks | Ramesh Johari |
| 3:00-5:00 PM | Poster Session and Reception |
