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

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

SUBMITTER: Smith MJ 

PROVIDER: S-EPMC11795351 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature