Harvard (bio)statisticians Miguel Hernan and Jamie Robins just released their new book, online and accessible for free! The Causal Inference book provides a cohesive presentation of causal inference, its concepts and its methods. The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from…

# Category: research

## Overviews of Graph Classification and Network Clustering methods

Thanks to Sebastian Raschka I am able to share this great GitHub overview page of relevant graph classification techniques, and the scientific papers behind them. The overview divides the algorithms into four groups: Factorization Spectral and Statistical Fingerprints Deep Learning Graph Kernels Moreover, the overview contains links to similar collections on community detection, classification/regression trees and gradient boosting papers…

## Causal Random Forests, by Mark White

I stumbled accros this incredibly interesting read by Mark White, who discusses the (academic) theory behind, inner workings, and example (R) applications of causal random forests: EXPLICITLY OPTIMIZING ON CAUSAL EFFECTS VIA THE CAUSAL RANDOM FOREST: A PRACTICAL INTRODUCTION AND TUTORIAL (By Mark White) These so-called “honest” forests seem a great technique to identify opportunities…

## Logistic regression is not fucked, by Jake Westfall

Recently, I came across a social science paper that had used linear probability regression. I had never heard of linear probability models (LPM), but it seems just an application of ordinary least squares regression but to a binomial dependent variable. According to some, LPM is a commonly used alternative for logistic regression, which is what…

## Propensity Score Matching Explained Visually

Propensity score matching (wiki) is a statistical matching technique that attempts to estimate the effect of a treatment (e.g., intervention) by accounting for the factors that predict whether an individual would be eligble for receiving the treatment. The wikipedia page provides a good example setting: Say we are interested in the effects of smoking on…

## People Analytics: Is nudging goed werkgeverschap of onethisch?

In Dutch only: Voor Privacyweb schreef ik onlangs over people analytics en het mogelijk resulterende nudgen van medewerkers: kleine aanpassingen of duwtjes die mensen in de goede richting zouden moeten sturen. Medewerkers verleiden tot goed gedrag, als het ware. Maar wie bepaalt dan wat goed is, en wanneer zouden werkgevers wel of niet mogen of…

## Glossary of Statistical Terminology

Frank Harrel shared this 16-page glossary of statistical terminology created by the Department of Biostatistics of Vanderbilt University School of Medicine. The overview touches on everything from Bayes’ Theorem to p-values, explaining matters in just the right detail. Various study designs and model types are also discussed so it might just come in handy for…

## Avoid bar plots for continuous data! Do this instead:

Tracey Weissgerber, Natasa Milic, Stacey Winham, and Vesna Garovic wrote this interesting 2015 paper on bar graphs. By a systematic review of physiology research, they demonstrate we need to reconsider how we present continuous data in small samples. Bar and line plots are commonly used to display continuous data. This is problematic, as many different data…

## Papers with Code: State-of-the-Art

OK, this is a really great find! The website PapersWithCode.com lists all scientific publications of which the codes are open-sourced on GitHub. Moreover, you can sort these papers by the stars they accumulated on Github over the past days. The authors, @rbstojnic and @rosstaylor90, just made this in their spare time. Thank you, sirs! Papers with Code allows you to quickly…