Causal Machine Learning

Causal machine learning aims to move beyond identifying correlations in data to establishing causal relationships, enabling prediction of outcomes under interventions and counterfactual scenarios. Current research focuses on improving the accuracy and interpretability of causal effect estimation, particularly for heterogeneous treatment effects, using methods like meta-learners, doubly robust estimators, and Bayesian causal forests, often applied within specific domains such as healthcare, supply chain management, and agriculture. This field is significant because it allows for more robust and reliable decision-making in various applications by providing a deeper understanding of cause-and-effect relationships, moving beyond simple prediction to informed intervention.