This article explores how multi-collinearity can damage causal inferences in marketing mix modeling and provides methods to address it, including Bayesian priors and random budget adjustments.
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.
This article discusses the differences between predictive and causal inference, explains why correlation does not imply causation, and why machine learning is not inherently suited for causal inference. It highlights the limitations of using machine learning for causal estimation and provides suggestions for when each type of inference should be used. The article also touches on causal machine learning and its role in addressing the challenges of high-dimensional data and complex functional forms.
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.
This article discusses causal inference, an emerging field in machine learning that goes beyond predicting what could happen to focus on understanding the cause-and-effect relationships in data. The author explains how to detect and fix errors in a directed acyclic graph (DAG) to make it a valid representation of the underlying data.