Removing confounding can be done via a variety methods including IP-weighting. This post provides a summary of the intuition behind IP-weighting.

Rebecca Barter

In my previous post, I introduced causal inference as a field interested in estimating the unobservable causal effects of a treatment: i.e. the difference between some measured outcome when the individual is assigned a treatment and the same outcome when the individual is not assigned the treatment.
If you’d like to quickly brush up on your causal inference, the fundamental issue associated with making causal inferences, and in particular, the troubles that arise in the presence of confounding, I suggest you read my previous post on this topic.

An introduction to the field of causal inference and the issues surrounding confounding.

Rebecca Barter

Often in science we want to be able to quantify the effect of an action on some outcome. For example, perhaps we are interested in estimating the effect of a drug on blood pressure. While it is easy to show whether or not taking the drug is associated with an increase in blood pressure, it is surprisingly difficult to show that taking the drug actually caused an increase (or decrease) in blood pressure.

A bunch of statisticians met to learn about ANOVA, a method that they're supposed to already know about. Here is my summary.

Rebecca Barter

Last week, practical statistics met to discuss all things ANOVA. Below you will find the slides from my talk, but read on if you would like to learn all about ANOVA.
When ANOVA is used and who uses it? ANOVA is used far and wide by the scientific community and beyond. Unfortunately, scientists also frequently misuse ANOVA. A study by Wu et al. (2011) showed that from a survey of 10 leading Chinese medical journals in 2008, 446 articles used ANOVA, and of those articles, 59% of them used ANOVA incorrectly.