pub struct PcAlgorithm {
pub alpha: f64,
pub max_cond_set_size: usize,
pub gaussian: bool,
}Expand description
Peter-Clark (PC) algorithm for constraint-based causal discovery.
Proceeds in two phases:
- Skeleton discovery: iteratively remove edges failing conditional
independence tests up to order
max_cond_set_size. - Orientation: orient colliders (v-structures) and apply Meek’s rules.
Fields§
§alpha: f64Significance level α for conditional independence tests.
max_cond_set_size: usizeMaximum conditioning set size.
gaussian: boolIf true, use Fisher’s z-transform (assumes Gaussian data).
Implementations§
Source§impl PcAlgorithm
impl PcAlgorithm
Sourcepub fn fit(
&self,
data: ArrayView2<'_, f64>,
var_names: &[&str],
) -> StatsResult<StructureLearningResult>
pub fn fit( &self, data: ArrayView2<'_, f64>, var_names: &[&str], ) -> StatsResult<StructureLearningResult>
Run the PC algorithm on the data matrix (rows = observations, cols = variables).
Returns a CPDAG (Completed Partially Directed Acyclic Graph).
Trait Implementations§
Auto Trait Implementations§
impl Freeze for PcAlgorithm
impl RefUnwindSafe for PcAlgorithm
impl Send for PcAlgorithm
impl Sync for PcAlgorithm
impl Unpin for PcAlgorithm
impl UnsafeUnpin for PcAlgorithm
impl UnwindSafe for PcAlgorithm
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