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,
What I Read: Five types of thinking
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 understand
What I Read: Be Careful Interpreting Predictive Models, Causal Insights
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What I Read: 3 Statistical Paradoxes
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 science
What I Read: Why machine learning struggles with causality
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?”