experiment – Andrew Fairless, Ph.D.

What I Read: myths, randomisation

By Andrew Fairless on February 18, 2025November 9, 2024

https://www.bps.org.uk/psychologist/dispelling-myths-about-randomisation Dispelling myths about randomisation14 October 2024 “The first myth is that randomisation works because it balances confounders…. This leads to the second myth, which is that we should testContinue readingWhat I Read: myths, randomisation

What I Read: passively learned, causality

By Andrew Fairless on December 4, 2024August 26, 2024

What can be passively learned about causality?Simons InstituteAndrew Lampinen (Google DeepMind)Jun 25, 2024 “What could language models learn about causality and experimentation from their passive training?”

What I Read: Supercharging A/B Testing

By Andrew Fairless on September 15, 2022August 3, 2022

Supercharging A/B Testing at Uber Supercharging A/B Testing at UberSergey Gitlin, Krishna Puttaswamy, Luke Duncan, Deepak Bobbarjung, and Arun Babu A S PJuly 21, 2022 “…building a correct and reliableContinue readingWhat I Read: Supercharging A/B Testing

What I Read: Experiment without the wait

By Andrew Fairless on March 28, 2022February 3, 2022

https://medium.com/pinterest-engineering/experiment-without-the-wait-speeding-up-the-iteration-cycle-with-offline-replay-experimentation-7a4a95fa674b Pinterest EngineeringJan 18Experiment without the wait: Speeding up the iteration cycle with Offline Replay ExperimentationMaxine Qian | Data Scientist, Experimentation and Metric Sciences“Online experimentation is often used to evaluateContinue readingWhat I Read: Experiment without the wait

What I Read: Compare ML Experiment Tracking Tools

By Andrew Fairless on April 21, 2021April 3, 2021

https://dagshub.com/blog/how-to-compare-ml-experiment-tracking-tools-to-fit-your-data-science-workflow/ How to Compare ML Experiment Tracking Tools to Fit Your Data Science WorkflowData Science WorkflowMar 22, 2021Guy SmoilovskyCo-Founder & CTO @ DAGsHub “There are many ML experiment tracking toolsContinue readingWhat I Read: Compare ML Experiment Tracking Tools

What I Read: Causal design patterns

By Andrew Fairless on April 1, 2021March 15, 2021

https://emilyriederer.netlify.app/post/causal-design-patterns/ Causal design patterns for data analystsJan 30, 2021 “…it’s easy to do causal inference wrong. For example, attempting to control for the wrong variables can sometimes induce correlations andContinue readingWhat I Read: Causal design patterns

What I Read: Switchback Tests and Randomized Experimentation Under Network Effects

By Andrew Fairless on February 8, 2021January 12, 2021

https://medium.com/@DoorDash/switchback-tests-and-randomized-experimentation-under-network-effects-at-doordash-f1d938ab7c2a Switchback Tests and Randomized Experimentation Under Network Effects at DoorDashDoorDashFeb 14, 2018·12 min readDavid Kastelman, Data Scientist & Raghav Ramesh,Machine Learning Engineer “…given the systemic nature of many ofContinue readingWhat I Read: Switchback Tests and Randomized Experimentation Under Network Effects

What I Read: Multi-Armed Bandits and Experimentation

By Andrew Fairless on January 23, 2021December 30, 2020

https://multithreaded.stitchfix.com/blog/2020/08/05/bandits/ Multi-Armed Bandits and the Stitch Fix Experimentation PlatformBlog post by Brian AmadioBrian AmadioAugust 05, 2020 “Multi-armed bandits have become a popular alternative to traditional A/B testing for online experimentationContinue readingWhat I Read: Multi-Armed Bandits and Experimentation

What I Read: To Adapt to Tech, We’re Heading Into the Shadows

By Andrew Fairless on December 30, 2020December 20, 2020

https://www.wired.com/story/opinion-to-adapt-to-tech-were-heading-into-the-shadows/ Matt BeaneIdeas06.08.2020 09:59 AMTo Adapt to Tech, We’re Heading Into the ShadowsMore and more innovation requires going into darker, more inappropriate, less ethical territory. Here’s how to respond. “…I’veContinue readingWhat I Read: To Adapt to Tech, We’re Heading Into the Shadows

Tag: experiment