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

Title:
Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial.
Authors:
Smith MJ; Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK., Mansournia MA; Department of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, Iran., Maringe C; Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK., Zivich PN; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.; Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Cole SR; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Leyrat C; Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK., Belot A; Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK., Rachet B; Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK., Luque-Fernandez MA; Inequalities in Cancer Outcomes Network, Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.; 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.
Source:
Statistics in medicine [Stat Med] 2022 Jan 30; Vol. 41 (2), pp. 407-432. Date of Electronic Publication: 2021 Oct 28.
Publication Type:
Journal Article; Observational Study; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: Chichester ; New York : Wiley, c1982-
MeSH Terms:
Models, Statistical* , Research Design*, Causality ; Computer Simulation ; Humans ; Probability ; Propensity Score
References:
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Grant Information:
MR/T032448/1 United Kingdom MRC_ Medical Research Council; MR/M013278/1 United Kingdom MRC_ Medical Research Council; 29018 United Kingdom CRUK_ Cancer Research UK; T32 HD091058 United States HD NICHD NIH HHS; MR/S01442X/1 United Kingdom MRC_ Medical Research Council; 18525 United Kingdom CRUK_ Cancer Research UK
Contributed Indexing:
Keywords: G-methods; causal inference; double-robust methods; g-formula; inverse probability weighting; machine learning; propensity score; regression adjustment; targeted maximum likelihood estimation
Entry Date(s):
Date Created: 20211029 Date Completed: 20220331 Latest Revision: 20250530
Update Code:
20250530
PubMed Central ID:
PMC11795351
DOI:
10.1002/sim.9234
PMID:
34713468
Database:
MEDLINE

Weitere Informationen

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.
(© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)