New working paper with Julian Schuessler on Graphical Causal Models for Survey Inference out on SocArXiv!
We demonstrate the usefulness of graphical causal models to communicate theoretical assumptions about the collection of survey data, to determine whether typical population parameters of interest to survey researchers can be recovered from a survey sample, and to support the choice of suitable adjustment strategies. Starting from graphical representations of prototypical selection…