Results for "treatment likelihood"

Expected causal effect of a treatment.

Probability of treatment assignment given covariates.

Probability of data given parameters.

Variable enabling causal inference despite confounding.

Estimating parameters by maximizing likelihood of observed data.

What would have happened under different conditions.

AI systems assisting clinicians with diagnosis or treatment decisions.

Patient agreement to AI-assisted care.

Legal right to fair treatment.

Predicting disease progression or survival.

Search algorithm for generation that keeps top-k partial sequences; can improve likelihood but reduce diversity.

Software regulated as a medical device.

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

Unequal performance across demographic groups.

Generates sequences one token at a time, conditioning on past tokens.

Exponential of average negative log-likelihood; lower means better predictive fit, not necessarily better utility.

Bayesian parameter estimation using the mode of the posterior distribution.

Monte Carlo method for state estimation.

Updated belief after observing data.

Inferring reward function from observed behavior.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).

Constraining outputs to retrieved or provided sources, often with citation, to improve factual reliability.

A model that assigns probabilities to sequences of tokens; often trained by next-token prediction.

A preference-based training method optimizing policies directly from pairwise comparisons without explicit RL loops.

Rules and controls around generation (filters, validators, structured outputs) to reduce unsafe or invalid behavior.

Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples.

Identifying and localizing objects in images, often with confidence scores and bounding rectangles.

Updating beliefs about parameters using observed evidence and prior distributions.

Categorizing AI applications by impact and regulatory risk.

Detecting unauthorized model outputs or data leaks.