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Causal Inference

Last updated Nov 14, 2024 Edit Source

Causal inference in machine learning refers to the process of identifying the causal relationships between variables in a dataset, often using techniques such as structural equation modeling (SEM), directed acyclic graphs (DAGs), and counterfactual reasoning. The goal is to estimate the causal effects of interventions or treatments on outcomes, allowing for the prediction of future behavior and decision-making under uncertainty. See Uplift modelling

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