Results for "causal identification"

Directed acyclic graph encoding causal relationships.

Formal model linking causal mechanisms and variables.

Learning physical parameters from data.

Training objective where the model predicts the next token given previous tokens (causal modeling).

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.

Expected causal effect of a treatment.

Variable enabling causal inference despite confounding.

Framework for reasoning about cause-effect relationships beyond correlation, often using structural assumptions and experiments.

Prevents attention to future tokens during training/inference.