CODE@MIT
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CODE@MIT
@codemit.bsky.social
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Conference on Digital Experimentation (CODE@MIT) hosted by
the MIT Initiative on the Digital Economy
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Reposted by CODE@MIT
bwaber.bsky.social
Next was an intriguing talk by Iavor Bojinov on the design and analysis of switchback and panel experiments at @codemit.bsky.social youtu.be/Uz1b0IYs_NU?... (5/11)
CODE@MIT 2025: Plenary Session 1
YouTube video by MIT Initiative on the Digital Economy
youtu.be
December 3, 2025 at 2:40 AM
Reposted by CODE@MIT
bwaber.bsky.social
Next was a great talk by Ramesh Johari on network congestion experiments and interference at @codemit.bsky.social www.youtube.com/watch?v=W5vW... (3/11)
CODE@MIT 2025: Plenary Session 2
YouTube video by MIT Initiative on the Digital Economy
www.youtube.com
December 3, 2025 at 2:40 AM
codemit.bsky.social
Videos from this year's plenary talks on digital experimentation are now available: www.youtube.com/@mitide/videos
December 8, 2025 at 2:39 PM
codemit.bsky.social
📢 Call for abstracts for the Conference on Digital Experimentation (CODE@MIT), which takes place Nov 14-15.

📨 Submit your best work by *Sept 12th*!
ide.mit.edu/events/2025-...

Here are three topics we're particularly interested in getting more submissions on this year:
With just ten days left until the final abstract deadline, I wanted to highlight some exciting research directions we're hoping to see at CODE 2025 (Nov 14-15 at MIT). This year, we are particularly interested in attracting submissions on the following topics: 1. Meta-experimental methodology and organizational learning from experimentation (e.g., how do systematic experimentation practices influence organizational decision-making? What factors drive institutional adoption of experimental methods? How can we develop frameworks for measuring the aggregate scientific and practical value of experimentation portfolios?) 2. Experimental evidence as foundational input for causal inference and predictive modeling (e.g., integrating randomized controlled trials with observational methods in econometric modeling, data-driven optimization, and synthetic control approaches; RCTs as inputs to media mix models; validation frameworks for surrogate endpoints and proxy measures) 3. AI and experimentation (e.g., AI-assisted experimental design and analysis; RCT-based evaluation frameworks for AI systems; using experiments to improve AI system performance; AI systems as experimental subjects or tools)
September 5, 2025 at 6:22 PM