Attribution Analysis
An analytical method that explains the driving factors behind predicted values or anomalies. Commonly used techniques include Shapley values, counterfactual reasoning, and feature importance ranking.
Timeline (last 90 days)
Locating the relevant neurons usually requires activation probes or attribution analysis, while verifying whether an edit truly establishes causality relies on ablation experiments and controlled experiments.
When designing tool-call logs, wrong tool selection and parameter errors should be recorded in separate tables; otherwise, subsequent attribution analysis cannot distinguish routing problems from argument-passing problems.
Rigorous attribution requires distinguishing three layers: the reasons companies themselves state, the net effect after excluding other variables, and the causal framing in media narratives.
Common attribution methods include Shapley values, counterfactual reasoning and feature importance ranking, which are often presented in prediction functions as contribution rankings or driver factor lists.
All Facts (4)
Locating the relevant neurons usually requires activation probes or attribution analysis, while verifying whether an edit truly establishes causality relies on ablation experiments and controlled experiments.
50%UnverifiedWhen designing tool-call logs, wrong tool selection and parameter errors should be recorded in separate tables; otherwise, subsequent attribution analysis cannot distinguish routing problems from argument-passing problems.
50%UnverifiedRigorous attribution requires distinguishing three layers: the reasons companies themselves state, the net effect after excluding other variables, and the causal framing in media narratives.
50%UnverifiedCommon attribution methods include Shapley values, counterfactual reasoning and feature importance ranking, which are often presented in prediction functions as contribution rankings or driver factor lists.
50%