class
CausalState:
Incremental causal analysis state (versioned events, no auto-rerun).
def
stale_query_count(self, /):
Number of registered queries whose cached results are currently stale.
def
stale_queries(self, /):
Raw ids of registered queries that are currently stale.
def
batch_ids(self, /):
Batch ids currently retained in Python (catalog order).
def
append_data(self, /, names, columns):
Append a tabular batch and retain its float64 columns.
Returns the new state version.
def
replace_data(self, /, names=None, columns=None):
Replace all retained batches; optionally load one new batch.
Returns the new state version.
def
get_batch(self, /, batch_id):
Fetch retained columns for batch_id as (names, list[ndarray]).
def
batch_nrows(self, /, batch_id):
Row count for a retained batch.
def
add_graph_evidence(self, /, evidence_id, fingerprint, bytes):
Add opaque graph evidence; returns new version.
def
graph_evidence(self, /):
List (id, fingerprint, bytes) graph-evidence records.
def
add_constraint(self, /, constraint_id, fingerprint):
Add a graph constraint; returns new version.
def
remove_constraint(self, /, constraint_id):
Remove a graph constraint by id; returns new version.
def
constraints(self, /):
List active constraint (id, fingerprint) pairs.
def
update_assumption(self, /, kind):
Update / insert a named assumption (default provenance: stated / identification / assumed).
def
register_average_effect(self, /, treatment, outcome):
Register a binary average-effect query; returns (version, query_id).
def
record_intervention(self, /, intervention_id, fingerprint):
Record an opaque intervention; returns new version.
def
refresh_results(self, /, entries):
Mark queries fresh at fingerprints: list of (query_id, fingerprint, bytes).
def
ols_ensure(self, /, key, ncols):
Ensure an OLS sufficient-stat slot exists with ncols predictors.
def
ols_append_row(self, /, key, row, y):
Append one OLS design row / response under key.
def
ols_get(self, /, key):
Return OLS summary dict for key: {n, ncols, xtx, xty, yty}.
def
cov_ensure(self, /, key, dim):
Ensure a streaming-covariance slot of dimension dim.
def
cov_update(self, /, key, row):
Observe one row into streaming covariance key.
def
cov_get(self, /, key):
Return streaming-cov summary {n, dim, mean, m2}.
def
particle_filter_init( self, /, key, n_particles, *, a=0.9, process_std=0.3, obs_std=0.5, seed=1):
Initialize a particle filter under key.
def
particle_filter_step(self, /, key, y):
Step particle filter key with observation y.
def
particle_filter_get(self, /, key):
Particle-filter summary {n_obs, n_particles, particles, log_weights}.
version
Monotonic state version (starts at 0; bumps on each applied event).
data_version
Current data-catalog version.