causal inference – Andrew Fairless, Ph.D.

What I Read: artificial intelligence really hard

By Andrew Fairless on August 16, 2023July 9, 2023

https://thegradient.pub/why-transformative-artificial-intelligence-is-really-really-hard-to-achieve/ Why transformative artificial intelligence is really, really hard to achieveArjun Ramani and Zhengdong Wang26.Jun.2023“1. The transformational potential of AI is constrained by its hardest problems

What I Read: missing data mechanisms

By Andrew Fairless on August 9, 2023July 9, 2023

https://cameronpatrick.com/post/2023/06/untangling-mar-mcar-mnar/ Understanding missing data mechanisms using causal DAGsCameron PatrickJune 28, 2023 “Correctly analysing datasets that have missing data requires extra care and consideration to produce correct results.”

What I Read: ELT Schedules, Root Cause Analysis

By Andrew Fairless on February 15, 2023December 16, 2022

https://www.montecarlodata.com/how-elt-schedules-can-improve-root-cause-analysis-for-data-engineers/ How ELT Schedules Can Improve Root Cause Analysis For Data EngineersRyan KearnsUpdated December 9, 2022 “In this article, Ryan Kearns… discusses the limitations of segmentation analysis when it comesContinue readingWhat I Read: ELT Schedules, Root Cause Analysis

What I Read: Causal Confounds, Sequential Decision

By Andrew Fairless on February 6, 2023December 4, 2022

https://blog.ml.cmu.edu/2022/11/28/causal-confounds-in-sequential-decision-making/ Causal Confounds in Sequential Decision MakingGokul SwamyNovember 28, 2022 “Using techniques from causal inference, we derive provably correct and scalable algorithms for sequential decision making in these sorts ofContinue readingWhat I Read: Causal Confounds, Sequential Decision

What I Read: Causal Inference

By Andrew Fairless on October 25, 2022September 9, 2022

https://thegradient.pub/causal-inference-connecting-data-and-reality/ Causal Inference: Connecting Data and RealityWenwen Ding04.Sep.2022 “Causal inference is a theory that describes, discriminates, and measures causal relationships, developed from statistics.”

What I Read: Statistical Critiques That Don’t Quite Work

By Andrew Fairless on March 29, 2022February 3, 2022

https://nickch-k.github.io/SomeThoughts/posts/2022-01-23-overdebunked/ Overdebunked! Six Statistical Critiques That Don’t Quite WorkWhen healthy skepticism of statistics turns into worse statistics (and an excuse).Nick Huntington-KleinJan. 23, 2022 “Skepticism about statistics is good. However, justContinue readingWhat I Read: Statistical Critiques That Don’t Quite Work

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: Causal Inference, Elasticity Pricing

By Andrew Fairless on July 23, 2021July 12, 2021

https://towardsdatascience.com/causal-inference-example-elasticity-de4a3e2e621b?gi=cb88b072c5b9 Causal Inference in the Wild: Elasticity PricingHow can machine learning to improve causal inference in industry? I describe a real example with code for retail pricing.Lars RoemheldJuly 8, 2021Continue readingWhat I Read: Causal Inference, Elasticity Pricing

What I Read: Double Machine Learning for causal inference

By Andrew Fairless on July 21, 2021July 2, 2021

https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Double Machine Learning for causal inferenceHow Double Machine Learning for causal inference works, from the theoretical foundations to an example of application.Borja VelascoJun 25 “This post tries to explain,Continue readingWhat I Read: Double Machine Learning for causal inference

What I Read: Five types of thinking

By Andrew Fairless on July 7, 2021June 22, 2021

https://towardsdatascience.com/five-types-of-thinking-for-a-high-performing-data-scientist-8ab70d70c23b?gi=772956cb941c Thinking about thinking (Part 1)Five types of thinking for a high performing data scientistFrom mental models to computational thinkingAnand S RaoApr 25 “As data scientists, we have to understandContinue readingWhat I Read: Five types of thinking

Tag: causal inference