Goetghebeur, E;
le Cessie, S;
De Stavola, B;
Moodie, EEM;
Waernbaum, I;
(2020)
Formulating causal questions and principled statistical answers.
Statistics in Medicine
10.1002/sim.8741.
(In press).
Preview |
Text
sim.8741.pdf - Published Version Download (1MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Formulating causal questions and principled statistical answers |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/sim.8741 |
| Publisher version: | https://doi.org/10.1002/sim.8741 |
| Language: | English |
| Additional information: | © 2020 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | causation, instrumental variable, inverse probability weighting, matching, potential outcomes, propensity score |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10111286 |
Archive Staff Only
 |
View Item |
