Photo by Ben White on Unsplash So here’s a mind blower: In some cases having more samples can actually reduce model performance. Don’t believe it? Neither did I! Read on to see how I demonstrate that phenomenon using a simulation study. Some cont...

R has many great tools for data wrangling. Two of those are the dplyr and data.table packages. When people wonder which one should they learn it is often argued that dplyr is considerably slower compared with data.table. Granted, data.table is blazing fast, but I personally find the ...

R has many great tools for data wrangling. Two of those are the dplyr and data.table packages. When people wonder which one should they learn it is often argued that dplyr is considerably slower compared with data.table. Granted, data.table is blaz...

It is not often that I find myself thinking “man, I wish we had in R that cool python library!”. That is however the case with the dowhy library which “provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts”. Luckily ...

It is not often that I find myself thinking “man, I wish we had in R that cool python library!”. That is however the case with the dowhy library which “provides a unified interface for causal inference methods and automatically tests many assumptio...

Intro We’ve seen on a previous post that one of the main differences between classic ML and Causal Inference is the additional step of using the correct adjustment set for the predictor features. In order to find the correct adjustment set we ne...

Intro We’ve seen on a previous post that one of the main differences between classic ML and Causal Inference is the additional step of using the correct adjustment set for the predictor features. In order to find the correct adjustment set we need...

I was really struggling with finding a header pic for this post when I came across the one above - titled “Dag scoring and selection” and since it’s sort of the topic of this post I decided to use it! Intro On my second post I’ve stressed ...

I was really struggling with finding a header pic for this post when I came across the one above - titled “Dag scoring and selection” and since it’s sort of the topic of this post I decided to use it! Intro On my second post I’ve stressed how ...

Intro I’ve discussed on several blog posts how Causal Inference involves making inference about unobserved quantities and distributions (e.g. we never observe \(Y|do(x)\)). That means we can’t benchmark different algorithms on Causal Inference tasks (e.g \(ATE/CATE\) estimation) the same way we do in ...

Intro I’ve discussed on several blog posts how Causal Inference involves making inference about unobserved quantities and distributions (e.g. we never observe \(Y|do(x)\)). That means we can’t benchmark different algorithms on Causal Inference tas...

Intro In this post I’ll make an exception and instead of sharing my research I’ll chime in on the never ending “R vs Python” squabble. The tl;dr version is: R is superior to Python for doing data science If you’re a newcomer to data ...

Intro In this post I’ll make an exception and instead of sharing my research I’ll chime in on the never ending “R vs Python” squabble. The tl;dr version is: R is superior to Python for doing data science If you’re a newcomer to data science yo...

Intro On my last few posts I’ve tried answering high level questions such as “What is Causal inference?”, “How is it different than ML?” and “When should I use it?”. In this post we finally get our hands dirty with some Kaggle style C...

Intro On my last few posts I’ve tried answering high level questions such as “What is Causal inference?”, “How is it different than ML?” and “When should I use it?”. In this post we finally get our hands dirty with some Kaggle style Causal Infe...

On my last post I gave an intuitive demonstration of what’s causal inference and how it’s different than classic ML. After receiving some feedback I realize that while the post was easy to digest, some confusion remains. In this post I’ll delve a bit deeper into what ...

On my last post I gave an intuitive demonstration of what’s causal inference and how it’s different than classic ML. After receiving some feedback I realize that while the post was easy to digest, some confusion remains. In this post I’ll delve a bit deeper into what ...

Intro Machine learning applications have been growing in volume and scope rapidly over the last few years. What’s Causal inference, how is it different than plain good ole’ ML and when should you consider using it? In this report I try giving a...

Machine learning applications have been growing in volume and scope rapidly over the last few years. What’s Causal inference, how is it different than plain good ole’ ML and when should you consider using it? In this report I try giving a short ...