Causal Inference

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Controlled experiment comparing variants by random assignment to estimate causal effects of changes.

Human or automated process of assigning targets; quality, consistency, and guidelines matter heavily.

Measure of consistency across labelers; low agreement indicates ambiguous tasks or poor guidelines.

Techniques to understand model decisions (global or local), important in high-stakes and regulated settings.

Studying internal mechanisms or input influence on outputs (e.g., saliency maps, SHAP, attention analysis).

Feature attribution method grounded in cooperative game theory for explaining predictions in tabular settings.

Local surrogate explanation method approximating model behavior near a specific input.