Results for "causal identification"

Directed acyclic graph encoding causal relationships.

Models effects of interventions (do(X=x)).

Formal model linking causal mechanisms and variables.

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

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

What would have happened under different conditions.

Learning physical parameters from data.

Variable enabling causal inference despite confounding.

Prevents attention to future tokens during training/inference.

Expected causal effect of a treatment.

Probability of treatment assignment given covariates.

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

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

Model relies on irrelevant signals.

Trend reversal when data is aggregated improperly.

A table summarizing classification outcomes, foundational for metrics like precision, recall, specificity.

Of true positives, the fraction correctly identified; sensitive to false negatives.

Of true negatives, the fraction correctly identified.

System for running consistent evaluations across tasks, versions, prompts, and model settings.

Identifying speakers in audio.

Decomposes a matrix into orthogonal components; used in embeddings and compression.

Predicting case success probabilities.

AI discovering new compounds/materials.

AI selecting next experiments.

Agents optimize collective outcomes.

Ensuring decisions can be explained and traced.