Educational notes: Introduction to computational causal inference using reproducible Stata, R and Python code
Matthew J. Smith (1) | Mohammad Ali Mansournia (2) | Camille Maringe (1) | Paul N. Zivich (3,4) | Stephen R. Cole (3) | Clemence Leyrat (1) | Aurelien Belot (1) | Bernard Rachet (1) | Miguel Angel Luque-Fernandez (*1,5,6) |
- Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology. London School of Hygieneand Tropical Medicine, London, U.K.
- Department of Epidemiology andBiostatistics. Tehran University of MedicalSciences, Tehran, Iran.
- Department of Epidemiology. University of North Carolina at Chapel Hill, North Carolina, U.S.
- Carolina Population Center. University ofNorth Carolina at Chapel Hill, North Carolina, U.S.
- Non-communicable Disease and Cancer Epidemiology Group, Instituto de investigacion Biosanitaria de Granada (ibs.GRANADA), Andalusian School of Public Health, University of Granada, Granada, Spain.
- Biomedical Network Research Centers of Epidemiology and Public Health (CIBERESP), Madrid, Spain.
Correspondence* Miguel Angel Luque-Fernandez, Email: miguel-angel.luque@lshtm.ac.uk
This repository makes available to the scientific community the data and code used in the manuscript available at
In research studies it can be unethical to assign a treatment to individuals in randomised controlled trials, instead observational data and an appropriate study design must be used. The purpose of many observational health studies is to estimate the effect of a treatment on an outcome which is causal. Although, there are major challenges with observational studies: one of which is confounding that can lead to biased estimates of the causal effect. Controlling for confounding is commonly performed by simple adjustment of measured confounders; although, sometimes this approach is suboptimal. 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 new estimators were developed to overcome the limitations of the previous estimators (i.e., non-parametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). Furthermore, we illustrate the use of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R and Python for researchers to adapt in their own observational study. The code can be accessed at
https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators
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