Formulating causal questions and principled statistical answers

Statistics in Medicine, vol. 39, pp. 4922–4948

Abstract

Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on www.ofcaus.org, where SAS and Stata code for analysis is also provided.

Authors 5

1. Els J. T. Goetghebeur corresponding

   Karolinska Institutet · Ghent University

   Affiliation as printed

   Department of Applied Mathematics, Computer Science and Statistics Ghent University Ghent Belgium

   Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden

   #N#Ghent University#N#

2. Saskia le Cessie Aachen Department of Clinical Epidemiology/Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands

   Leiden University Medical Center

   Affiliation as printed

   Department of Clinical Epidemiology/Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands

   Department of Clinical Epidemiology/Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands

3. Bianca Lucia De Stavola

   University College London

   Affiliation as printed

   Great Ormond Street Institute of Child Health University College London London UK

   Great Ormond Street Institute of Child Health, University College London London UK

4. Erica E. M. Moodie

   McGill University

   Affiliation as printed

   Division of Biostatistics McGill University Montreal Quebec Canada

   Division of Biostatistics McGill University Montreal Quebec Canada

5. Ingeborg Waernbaum

   Uppsala University

   Affiliation as printed

   Department of Statistics Uppsala University Uppsala Sweden

   Department of Statistics , Uppsala University , Uppsala , Sweden