/ Recent content on Michael Ore Hugo -- gohugo.io Copyright © 2018 - Michael Ore Mon, 22 Jan 2018 00:00:00 +0000 /projects/ Mon, 22 Jan 2018 00:00:00 +0000 /projects/ Class project - Predicting feed intake in dairy cattle from genomic data Prediction of feed intake of dairy cattle based on a large genomic data set. Report Undergraduate senior project - Detecting sparse observation zones Development of a novel algorithm to find large regions with a small number of data points. /about/ Sat, 20 Jan 2018 00:00:00 +0000 /about/ Hello, I’m Michael Ore, and I’m a software developer. I’m currently studying at UW-Madison for a role change into data science. LinkedIn GitHub /posts/instrument/ Thu, 01 Mar 2018 00:00:00 +0000 /posts/instrument/ Now we consider the problem of measuring the strength of causal effects in the presence of confounding variables. As an example, let X be whether a person is a veteran and Y be their income. /posts/common-effect/ Wed, 28 Feb 2018 00:00:00 +0000 /posts/common-effect/ In the last post we looked at causal effects based on intermediate causes and common causes, both of which introduce a correlation that goes away when the “middle” variable is conditioned on. /posts/intermediate-common/ Fri, 26 Jan 2018 00:00:00 +0000 /posts/intermediate-common/ We’ll continue with our example of the relationship between IQ (X) and income (Y). We’ll set aside models B (income causes IQ) and C (no relationship) and compare our model A (IQ causes income) with two new models D and E that include a third variable of years in school (Z). /posts/causal-networks/ Thu, 25 Jan 2018 00:00:00 +0000 /posts/causal-networks/ As we discussed in the last post, Bayesian networks can often be written in multiple equivalent ways by reversing some of the arrows. Generally the most natural choice has arrows matching the causal direction of the effects. /posts/bayesian-networks/ Sun, 21 Jan 2018 00:00:00 +0000 /posts/bayesian-networks/ I’ve found the concept of causal models as described in e.g. Pearl’s Causality 2009 to be a very useful tool for statistical thinking. This is the beginning of a series of posts introducing the concept and its applicability.