Statistical modeling often involves two parts, model selection and model inference. As it turns out, many models have a causal interpretation. For example, a si

Statistical modeling often involves two parts, model selection and model inference. As it turns out, many models have a causal interpretation. For example, a simple linear model could be interpreted as x causes y using the equation y = b*x + error. If you get a bunch of variables and relate them through linear equations where some cause others plus some error, then different patterns of causal relations imply different covariance matrices. Classically, people have used these covariance matrices to choose between possible causal models. There are different approaches, but a common one in the behavioral sciences is to choose a few causal models to represent theories, and then perform model selection (like choosing between multiple regression models with different variables). To answer your question, though, instrumental variables are a specific causal pattern in a model, but there can be other models, such as those with latent variables.