Abstract
Confounding can significantly distort the findings of studies examining cause-and-effect relationships, especially in etiological research. To mitigate this issue, researchers must carefully assess potential confounding variables that may relate to both the exposure and outcome but are not directly influenced by the exposure itself. It is essential that these variables truly impact the outcome rather than simply being correlated with the exposure to avoid false associations. Strengthening confidence in the actual relationship between exposure and outcome requires an understanding of biological mechanisms and the application of various methods to adjust for confounders. The oversight of confounding often arises from inappropriate statistical tests and the aggregation of data across multiple studies. This scoping review article discusses the challenges posed by confounding and presents machine learning approaches for effective control in health science research. Directed acyclic graphs (DAGs) serve as causal graph tools to identify potential confounding variables in health research. By mapping presumed relationships between variables, DAGs enable researchers to estimate causal effects more accurately. While traditional methods such as randomization, matching, and stratification remain effective for controlling confounding, newer techniques like latent variable modeling with negative controls and machine learning methods such as LASSO, Ridge regression, and random forests offer enhanced flexibility and adaptability.