Causal Inference
A method in AI and statistics used to determine cause-and-effect relationships, helping to understand the impact of interventions or changes in variables.
Definition
Goes beyond correlation by using techniques (e.g., randomized trials, instrumental variables, propensity scores) to isolate the effect of a single factor. In AI, causal models help predict what will happen if you change a policy or feature, enabling decision-makers to act on reliable “what-if” insights rather than mere associations.
Real-World Example
A medical research team uses causal inference on patient data to determine that reducing a specific medication dosage by 10% causes a 5% drop in side-effect rates—guiding safer prescription guidelines.
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