This article explains how adding monotonic constraints to traditional ML models can make them more reliable for causal inference, illustrated with a real estate example.
Exploring and exploiting the seemingly innocent theorem behind Double Machine Learning. The theorem, rooted in econometrics, states that if we have a linear model that predicts an outcome variable based on multiple features, and we want to understand the causal effect of a specific feature on the outcome, we can use the residuals of the model as an instrumental variable to estimate the causal effect.
