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

Area of Science:

• Epidemiology
• Biostatistics
• Health Services Research

Background:

• Observational studies are crucial when randomization is not feasible for estimating treatment effects.
• Confounding is a major challenge in observational studies, often inadequately addressed by simple adjustment methods.
• Existing causal inference methods require accessible computational tutorials for applied researchers.

Purpose of the Study:

• To provide a computational tutorial on various causal inference estimators.
• To illustrate the implementation of estimators like g-formula, inverse probability weighting, and double-robust methods.
• To offer reproducible code in Stata, R, and Python for applying these methods to observational data.

Main Methods:

• Historical overview of causal inference estimators, highlighting advancements in addressing confounding.
• Demonstration of nonparametric and parametric g-formula, inverse probability weighting, and data-adaptive estimators.
• Application of methods to an empirical example from the Connors intensive care medicine study.

Main Results:

• The tutorial provides a clear computational implementation of advanced causal inference techniques.
• Reproducible code is supplied, enabling researchers to apply these methods to their own observational studies.
• The Connors study example serves to illustrate the practical application and comparison of different estimators.

Conclusions:

• Accessible computational tools are essential for the widespread adoption of causal inference methods.
• This tutorial bridges the gap between theoretical causal inference and practical application in observational research.
• Researchers can leverage the provided code to improve the validity of treatment effect estimates from observational data.