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Observational Study
. 2022 Jan 30;41(2):407-432.
doi: 10.1002/sim.9234. Epub 2021 Oct 28.

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial

Matthew J Smith  1 , Mohammad A Mansournia  2 , Camille Maringe  1 , Paul N Zivich  3   4 , Stephen R Cole  3 , Clémence Leyrat  1 , Aurélien Belot  1 , Bernard Rachet  1 , Miguel A Luque-Fernandez  1   5   6
Affiliations
Observational Study

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial

Matthew J Smith et al. Stat Med. .

Abstract

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization 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 (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation 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: G-methods; causal inference; double-robust methods; g-formula; inverse probability weighting; machine learning; propensity score; regression adjustment; targeted maximum likelihood estimation.

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Figures

FIGURE 1
FIGURE 1
Y: outcome; A: treatment; W: sufficient set of variables to control for confounding, as outlined in Connors et al
FIGURE 2
FIGURE 2
Propensity score overlap by treatment status
See this image and copyright information in PMC

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