directed acyclic graph – Andrew Fairless, Ph.D.

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: 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: Data Orchestration w/ Nick Schrock (Elementl)

By Andrew Fairless on November 10, 2021October 24, 2021

https://youtu.be/jRrx8Vvjae0 DC_THURS on Data Orchestration w/ Nick Schrock (Elementl)Streamed live on Jun 24, 2021Data Council“Nick is the founder of Elementl and the original author of the dagster data orchestration project.Continue readingWhat I Read: Data Orchestration w/ Nick Schrock (Elementl)

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: How Build Production Workflow SQL

By Andrew Fairless on February 22, 2021January 27, 2021

https://shopify.engineering/build-production-grade-workflow-sql-modelling How to Build a Production Grade Workflow with SQL Modellingby Michelle Ark Data Science & EngineeringNov 19, 2020 “We’ll show you how we moved to a SQL modelling workflowContinue readingWhat I Read: How Build Production Workflow SQL

What I Read: How DAGs grow

By Andrew Fairless on February 14, 2021January 18, 2021

https://greatexpectations.io/blog/deeper-wider-thicker/ How DAGs grow: Deeper, wider, and thickerNovember 16, 2020 “Over time, this turns into a steady, compounding creep in the maintenance time that you’re putting into the project…. StuffContinue readingWhat I Read: How DAGs grow

What I Read: Nitpicking ML Technical Debt

By Andrew Fairless on January 2, 2021December 22, 2020

https://matthewmcateer.me/blog/machine-learning-technical-debt/ Nitpicking Machine Learning Technical DebtRevisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)May 10, 2020 “Now the Tech Debt Paper is makingContinue readingWhat I Read: Nitpicking ML Technical Debt

What I Read: Learning Neural Causal Models

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

https://arxiv.org/abs/1910.01075Learning Neural Causal Models from Unknown InterventionsNan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio “We present a new framework for meta-learning causal modelsContinue readingWhat I Read: Learning Neural Causal Models

Tag: directed acyclic graph