Tutorial: Introduction to computational causal inference for applied researchers and epidemiologists
Matthew James Smith, Camille Maringe, Bernard Rachet, Mohammad A. Mansournia, Paul Zivich, Stephen R. Cole, Miguel Angel Luque Fernandez
This repository makes available to the scientific community the data and code used in the preprint manuscript available at
CITE this repository:
Matthew James Smith, Camille Maringe, Bernard Rachet, Mohammad A. Mansournia, Paul Zivich, Stephen R. Cole, Miguel Angel Luque Fernandez
ABSTRACT
The purpose of many health studies is to estimate the effect of an exposure on an outcome. It is not always ethical to assign an exposure to individuals in randomised controlled trials, instead observational data and appropriate study design must be used. There are major challenges with observational studies, one of which is confounding that can lead to biased estimates of the causal effects. Controlling for confounding is commonly performed by simple adjustment for measured confounders; although, often this is not enough. Recent advances in the field of causal inference have dealt with confounding by building on classical standardisation methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where different estimators were developed to overcome the limitations of the previous one. Furthermore, we also briefly introduce the potential outcomes framework, illustrate the use of different methods using an illustration from the health care setting, and most importantly, we provide reproducible and commented code in Stata, R and Python for researchers to apply in their own observational study. The code can be accessed at
https://github.com/migariane/TutorialCausalInferenceEstimators
KEYWORDS: Causal Inference; Regression adjustment; G-methods; G-formula; Propensity score; Inverse probability weighting; Double-robust methods; Machine learning; Targeted maximum likelihood estimation; Epidemiology; Statistics; Tutorial