Making Causal Claims as a Data Scientist: Tips and Tricks Using R

Abstract

Making believable causal claims can be difficult, especially with the much repeated adage “correlation is not causation”. This talk will walk through some tools often used to practice safe causation, such as propensity scores and sensitivity analyses. In addition, we will cover principles that suggest causation such as the understanding of counterfactuals, and applying Hill’s criteria in a data science setting. We will walk through specific examples, as well as provide R code for all methods discussed.

Date
Location
Austin, Texas

Making Causal Claims as a Data Scientist: Tips and Tricks Using R from Lucy D'Agostino McGowan