My story is about multi-armed bandits and why data collected with bandits lead to underestimated means. If you work with online data (webshops, ads, mobile apps, etc.), using multi-armed bandit algorithms can speed up your testing process. However, measuring the outcomes of the different versions is not that straight-forward. This post is published at Emarsys Craftlab, you can read it there.

In the age of big data, everyone had heard sentences about something being statistically significant or insignificant. But significantly less people are able to explain what it truly means. Research shows that even people with formal statistical training misinterpret statistical significance. This post published at Emarsys Craftlab is my (in)significant attempt to clear things up.

The standard practice of deciding about controls -- running a t-test on the potential control -- is very dangerous. More specifically, it leads to biased estimates and too optimistic standard errors. We can avoid these problems by taking into account the correlation between the treatment and the control variables as well (so called double-selection).

Receiver Operating Characteristic curve is a great tool to visually illustrate the performance of a binary classifier. It plots the true positive rate (TPR) or the sensitivity against the false positive rate (FPR) or 1 - specificity. Usually, the algorithm gives you a probability (e.g. simple logistic regresssion), so for …

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Instrumental variables are often used in causal analysis when randomized control trials are out of option. However, it is not always emphasized that the instrumental variable estimator -- even if the instrument is valid and relevant -- does not necessarily identify the average treatment effect (ATE) or the average treatment effect on the treated (ATET); most often, it only identifies the local average treatment effect (LATE): the average treatment effect on the complier subpopulation (see Angrist&Imbens, 1994).

Assume I have a file with downloaded articles with a similar structure as follows:

head -2 file-with-dates.csv
title,url,tag,date
"title of the article",http://www.url-of-the-article.com,"tag1,etc",2015-02-27

How could I calculate the number of articles posted before a certain date without having to load …