AI Glossary Term

What does “Causal Inference” mean?

Definition

Methods for estimating cause-and-effect relationships rather than simple correlations.

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How Causal Inference works

Example

A hospital notices patients on a drug do better. Causal methods ask whether the drug caused the improvement or whether healthier patients were simply more likely to get it, using trial designs or statistical adjustments.

Why it matters

Predictions that work on past patterns can fail when you intervene. Cause-and-effect questions need different methods than prediction.

Common questions

Why not just use correlations?

Two things can move together because of a third factor, so acting on a correlation may not change the outcome.

What is the gold standard?

A randomized controlled experiment, which breaks links between treatment and other factors.

Can machine learning do it?

It can help estimate effects, but needs assumptions about how data was generated.

On-Device AIAI inference performed locally on user hardware rather than in a remote cloud service.Semantic SearchSearch that matches meaning rather than exact keyword overlap, often using embeddings.Baseline ModelA simple reference model used to compare whether more complex approaches actually improve results.Domain AdaptationMethods that transfer a model trained in one domain to perform better in another domain.InferenceThe runtime phase where a trained model generates predictions or outputs.Inference-Time ComputeThe amount of processing power consumed while producing each response.

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