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Aleix Ruiz de Villa
Aleix Ruiz de Villa

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Published in Towards Data Science

·Feb 1, 2021

Propensity Scores and Inverse Probability Weighting in Causal Inference

A global overview — This is a joint work with Núria Correa Mañas, Jesus Cerquides, Joan Capdevila Pujol and Borja Velasco within the Causal ALGO Bcn. You can find a hands-on post by Núria Correa Mañas here! In this post we are going to talk about two well known techniques used to calculate Average…

Causal Inference

7 min read

Propensity Scores and Inverse Probability Weighting in Causal Inference
Propensity Scores and Inverse Probability Weighting in Causal Inference

Dec 12, 2019

Causality: AB Testing + Causal Inference

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!

Ab Testing

1 min read

Causality: AB Testing + Causal Inference
Causality: AB Testing + Causal Inference

Published in Towards Data Science

·Sep 12, 2019

What to expect from a causal inference business project: an executive’s guide III

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. …

Machine Learning

7 min read

What to expect from a causal inference business project: an executive’s guide III
What to expect from a causal inference business project: an executive’s guide III

Published in Towards Data Science

·Sep 12, 2019

What to expect from a causal inference business project: an executive’s guide II

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…

Machine Learning

4 min read

What to expect from a causal inference business project: an executive’s guide II
What to expect from a causal inference business project: an executive’s guide II

Published in Towards Data Science

·Sep 12, 2019

What to expect from a causal inference business project: an executive’s guide I

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…

Data Science

6 min read

What to expect from a causal inference business project: an executive’s guide I
What to expect from a causal inference business project: an executive’s guide I

Published in Towards Data Science

·Feb 20, 2019

Solving Simpson’s Paradox

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…

Data Science

6 min read

Solving Simpson’s Paradox
Solving Simpson’s Paradox

Published in Towards Data Science

·Dec 9, 2018

Observing is not intervening

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…

Data Science

4 min read

Observing is not intervening
Observing is not intervening

Published in Towards Data Science

·Nov 19, 2018

Use causal graphs!

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…

Data Science

6 min read

Use causal graphs!
Use causal graphs!

Published in Towards Data Science

·Nov 10, 2018

Why do we need causality in data science?

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…

Data Science

4 min read

Why do we need causality in data science?
Why do we need causality in data science?
Aleix Ruiz de Villa

Aleix Ruiz de Villa

Phd in mathematics and data scientist. Freelance consultant.

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