Collider Bias from Unstructured Data Covariates

We explore the significant risk of collider bias when using covariates derived from unstructured data, such as text or images, in causal analysis. It explains that collider bias occurs when adjusting for a variable that is a common effect of two or more other variables, including the exposure and outcome of interest or their correlates. The text details how features from unstructured data, characterized by high dimensionality, opacity, and being engineered constructs, are particularly susceptible to being colliders. Numerous examples across different domains illustrate how conditioning on these features can lead to spurious associations and biased causal estimates, emphasizing the need for explicit causal reasoning and careful covariate selection in such research.