Formulating causal questions and principled statistical answers

TL;DR

This paper provides an introductory overview of causal inference methods, emphasizing the importance of clearly formulated causal questions, and illustrates their application through simulation in observational and experimental contexts.

Contribution

It offers a principled framework for defining and estimating causal effects using potential outcomes, with guidance on choosing appropriate methods and illustrating with simulation examples.

Findings

Clarifies causal question formulation and relevant effect definitions.

Demonstrates application of causal methods through simulation.

Provides open-source code for data generation and analysis.

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 discuss 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 that mimic those from an observational or randomised 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.