Publications by R on Just be-cause
“Real life” DAG simulation using the simMixedDAG package
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 ML because we don’t have any ground tru...
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Automatic DAG learning – part 1
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 important it is to use the correct adjustment set when trying to estimate a causal relationshi...
6477 sym 18 img 2 tbl
Automatic DAG learning – part 1
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 important it is to use the correct adjustment set when trying to estimate a causal relationshi...
6477 sym 18 img 2 tbl
Automatic DAG learning – part 2
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 a DAG that represents the relationships between all features relevant to our problem. One way of o...
6017 sym R (638 sym/1 pcs) 16 img
Automatic DAG learning – part 2
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 a DAG that represents the relationships between all features relevant to our problem. One way of o...
6017 sym R (638 sym/1 pcs) 16 img
dowhy library exploration
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 enough though, the awesome fol...
4910 sym R (5865 sym/16 pcs) 6 img 1 tbl
dowhy library exploration
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 enough though, the awesome fol...
4914 sym R (5868 sym/16 pcs) 6 img 1 tbl
dtplyr speed benchmarks
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 syntax hard and un-intuitive and the speed difference doesn’t ...
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dtplyr speed benchmarks
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 syntax hard and un-intuitive and the speed difference doesn’t ...
4031 sym R (8715 sym/6 pcs) 4 img
Don’t be fooled by the hype python’s got
R still R still is the tool you want We all know python popularity among DS practitioners has soared over the past few years, signaling both aspiring DS on the one hand and organizations on the other to favor python over R in a snowballing dynamic. I’m writing this post to help turn the tide and let us all keep writing in the language we love ...
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