# Causal Discovery

Observational data often leave causal directions unresolved or unstable, and large language models can propose directions from variable names and descriptions. This study splits the use of semantic information into query allocation, proposal admission and graph retention, and tests each on the Asia, Sachs, Child, Insurance and Alarm benchmark networks. Semantic orientation beats coin-flip orientation, but targeting unstable edges shows no established advantage over matched-random allocation; the tested admission certificate can only abstain given the available calibration tasks, and learned references contain the true DAG in only three of 80 planned tasks.

Observational data often leave causal directions unresolved or unstable, and large language models can propose directions from variable names and descriptions. This study splits the use of semantic information into query allocation, proposal admission and graph retention, and tests each on the Asia, Sachs, Child, Insurance and Alarm benchmark networks. Semantic orientation beats coin-flip orientation, but targeting unstable edges shows no established advantage over matched-random allocation; the tested admission certificate can only abstain given the available calibration tasks, and learned references contain the true DAG in only three of 80 planned tasks.

Constitutive model discovery now asks which environmental variable drives which mechanism, not merely which candidate terms fit the response. A constitutive model with internal variables is written as a time-unrolled structural causal model, candidate graphs are scored by a weak-form equilibrium gap through the boundary value problem, and climate-chamber protocols count as interventions. On a synthetic perforated plate at about 0.01 px displacement noise, interventional data recover the true graph exactly in 7 of 10 runs, with no attribution error; correlated observational data never do.

Constitutive model discovery now asks which environmental variable drives which mechanism, not merely which candidate terms fit the response. A constitutive model with internal variables is written as a time-unrolled structural causal model, candidate graphs are scored by a weak-form equilibrium gap through the boundary value problem, and climate-chamber protocols count as interventions. On a synthetic perforated plate at about 0.01 px displacement noise, interventional data recover the true graph exactly in 7 of 10 runs, with no attribution error; correlated observational data never do.