The intuition behind inverse probability weighting in causal inference

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.

Confounding in causal inference: what is it, and what to do about it?

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.

Migrating from GitHub Pages to Netlify: how and why?

Sorry GitHub Pages, but we need to break up. I've found a new web hosting service.

Rebecca Barter

Goodbye GitHub Pages, I hope you’re not too upset! After finding myself increasingly frustrated with GitHub Pages inability to cooperate with my website engine of choice, Hugo, I’ve decided to make a move. Here are the reasons why: Having all of my project pages being subpages of my personal page felt weird. Dealing with git subtrees, merge conflicts on the master branch, and having to do all kinds of work-arounds to get Hugo to play nice with GitHub Pages was driving me crazy.