Introductory talk at BcnAnalytics Bartek Skorulski and myself we gave an introductory talk at one of the regular Barcelona’s events of BcnAnalytics. We focused on how combining both techniques, AB Testing and Causal Inference, can give a comprehensive solution to causality problems in businesses. You can see the event here!

Part III: Where causal inference stands in the current AI, Big Data, Data Science, Statistics, and Machine Learning scene? — This is the third part of the post “What to expect from a causal inference business project: an executive’s guide”. You will find the second one here. Most of these words have fuzzy meaning, at least at a popular level. …

Part II: Which are the project key points you need to know — This is the second part of the post “What to expect from a causal inference business project: an executive’s guide”. You will find the third part here. Causal Modeling Casual inference models how variables affect each other. Based on this information, uses some calculation tools to answer questions like what would have…

Part I: When do you need casual inference? — This is the fifth post on a series about causal inference and data science. The previous one was “Solving Simpson’s Paradox”. You will find the second part of this post here. Causal inference is a new language to model causality to help understand better causes and impacts so that we…

Understand a key toy example in causal inference — This is the fourth post on a series about causal inference and data science. The previous one was “Observing is not intervening”. Simpson’s paradox is a great example. At first, it challenges our intuition, but then, if we are able to dissect it properly, gives a lot of ideas about…

And why conditional probabilities are not enough — This is the third post on a series about causal inference and data science. The previous one was “Use causal graphs!” and the next one is “Solving Simpson’s Paradox”. In causal inference we are interested in measuring the effect that a variable A , say a treatment for some particular…

This is the second post of a series about causality in data science. You can check the first one “Why do we need causality in data science?” and the next one “Observing is not intervening”. As we said, there are currently two principal frameworks for working with causality: potential outcomes…

This is a series of posts explaining why we need causal inference in data science and machine learning (next one is ‘Use Graphs!’). Causal inference brings a new fresh set of tools and perspectives that let us deal with old problems. When experimenting is not available First off, designing and running experiments (typically with A/B…