This post uses a synthetic control design to study whether Texas's prison building boom in 1993 resulted in them incarcerating more prisoners than they would have if their rate of prison building had continued as normal. The analysis will build off the one in the book Causal Inference: The Mixtable …
read moreMatching in Observational Studies
A 'matching' quasi-experimental design controls for confounder variables \(x\) by estimating what the control outcomes \(y\) would be if the control population had the same values of \(x\) as the treatment population. To do this, we regress outcomes in the control population on \(x\), and apply this regression model to …
read moreHop Lists
Hop Lists are a novel retroactive set data-structure that allow for a branching timeline.
read moreGraph SLAM
For a robot to navigate autonomously, it needs to learn both its own location, as well as the locations of any potential obsticles around it, given its sensors' observations of the world. We'll create a probabilistic model of our environment and get a MAP estimate of these unknown quantities.
- Let …
Diagnosing Lack of Independence in Exogenous Variables
This post outlines a simple workflow for diagnosing lack of independence in
read morestatsmodels.Finite Basis Gaussian Processes
By Mercer's theorem, every positive definite kernel \(k(x, y) : \mathcal{X} \to \mathcal{X} \to \mathbb{R}\) that we might want to use in a Gaussian Process corresponds to some inner product \(\langle \phi(x), \phi(y) \rangle\), where \(\phi : \mathcal{X} \to \mathcal{V}\) maps our inputs into …
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