causal inference – Page 2 – Andrew Fairless, Ph.D.

What I Read: Be Careful Interpreting Predictive Models, Causal Insights

By Andrew Fairless on June 9, 2021May 30, 2021

https://towardsdatascience.com/be-careful-when-interpreting-predictive-models-in-search-of-causal-insights-e68626e664b6 Be Careful When Interpreting Predictive Models in Search of Causal InsightsA careful exploration of the pitfalls of trying to extract causal insights from modern predictive machine learning models.Scott LundbergContinue readingWhat I Read: Be Careful Interpreting Predictive Models, Causal Insights

What I Read: 3 Statistical Paradoxes

By Andrew Fairless on May 13, 2021April 17, 2021

https://towardsdatascience.com/top-3-statistical-paradoxes-in-data-science-e2dc37535d99 Top 3 Statistical Paradoxes in Data ScienceObservation bias and sub-group differences generate statistical paradoxes.Francesco Casalegno “Observation bias and sub-group differences can easily produce statistical paradoxes in any data scienceContinue readingWhat I Read: 3 Statistical Paradoxes

What I Read: Why machine learning struggles with causality

By Andrew Fairless on April 28, 2021April 10, 2021

https://bdtechtalks.com/2021/03/15/machine-learning-causality/ Why machine learning struggles with causalityBen DicksonMarch 15, 2021 “Why do machine learning models fail at generalizing beyond their narrow domains and training data?”

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: Causal Reasoning in Probability Trees

By Andrew Fairless on February 11, 2021January 13, 2021

https://arxiv.org/abs/2010.12237 Algorithms for Causal Reasoning in Probability TreesTim Genewein, Tom McGrath, Grégoire Déletang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega “Probability trees are one of the simplest modelsContinue readingWhat I Read: Causal Reasoning in Probability Trees

What I Read: Explainable AI, 2-Stage Approach

By Andrew Fairless on February 5, 2021January 10, 2021

https://www.kdnuggets.com/2020/10/explaining-explainable-ai.html Explaining the Explainable AI: A 2-Stage ApproachBy Amit Dhurandhar, Research Scientist at IBM. “XAI is primarily concerned with understanding or interpreting the decisions made by these opaque or black-boxContinue readingWhat I Read: Explainable AI, 2-Stage Approach

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

What I Read: Replacing do-calculus with Bayes rule

By Andrew Fairless on December 8, 2020November 29, 2020

https://arxiv.org/abs/1906.07125 Replacing the do-calculus with Bayes ruleFinnian Lattimore, David Rohde “…while it is critical to explicitly model our assumptions on the impact of intervening in a system, provided we doContinue readingWhat I Read: Replacing do-calculus with Bayes rule

Tag: causal inference