oxirs_embed/

causal_representation_learning.rs

//! Causal Representation Learning
//!
//! This module implements causal representation learning for discovering and learning
//! causal structures in embedding spaces with interventional learning, structural
//! causal models, and counterfactual reasoning capabilities.

use crate::{EmbeddingModel, ModelConfig, TrainingStats, Triple, Vector};
use anyhow::{anyhow, Result};
use async_trait::async_trait;
use chrono::Utc;
use scirs2_core::ndarray_ext::{Array1, Array2};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use uuid::Uuid;

/// Configuration for causal representation learning
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct CausalRepresentationConfig {
    pub base_config: ModelConfig,
    /// Causal discovery configuration
    pub causal_discovery: CausalDiscoveryConfig,
    /// Structural causal model configuration
    pub scm_config: StructuralCausalModelConfig,
    /// Interventional learning configuration
    pub intervention_config: InterventionConfig,
    /// Counterfactual reasoning configuration
    pub counterfactual_config: CounterfactualConfig,
    /// Disentanglement configuration
    pub disentanglement_config: DisentanglementConfig,
}

/// Causal discovery configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CausalDiscoveryConfig {
    /// Discovery algorithm
    pub algorithm: CausalDiscoveryAlgorithm,
    /// Significance threshold for causal relationships
    pub significance_threshold: f32,
    /// Maximum number of parents per variable
    pub max_parents: usize,
    /// Use interventional data
    pub use_interventions: bool,
    /// Constraint-based settings
    pub constraint_settings: ConstraintSettings,
    /// Score-based settings
    pub score_settings: ScoreSettings,
}

impl Default for CausalDiscoveryConfig {
    fn default() -> Self {
        Self {
            algorithm: CausalDiscoveryAlgorithm::PC,
            significance_threshold: 0.05,
            max_parents: 5,
            use_interventions: true,
            constraint_settings: ConstraintSettings::default(),
            score_settings: ScoreSettings::default(),
        }
    }
}

/// Causal discovery algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CausalDiscoveryAlgorithm {
    /// PC algorithm (constraint-based)
    PC,
    /// Fast Causal Inference (FCI)
    FCI,
    /// Greedy Equivalence Search (GES)
    GES,
    /// Linear Non-Gaussian Acyclic Model (LiNGAM)
    LiNGAM,
    /// NOTEARS (continuous optimization)
    NOTEARS,
    /// DirectLiNGAM
    DirectLiNGAM,
    /// Causal Additive Models (CAM)
    CAM,
}

/// Constraint-based algorithm settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConstraintSettings {
    /// Independence test type
    pub independence_test: IndependenceTest,
    /// Alpha level for tests
    pub alpha: f32,
    /// Use stable PC algorithm
    pub stable: bool,
    /// Maximum conditioning set size
    pub max_cond_set_size: usize,
}

impl Default for ConstraintSettings {
    fn default() -> Self {
        Self {
            independence_test: IndependenceTest::PartialCorrelation,
            alpha: 0.05,
            stable: true,
            max_cond_set_size: 3,
        }
    }
}

/// Independence tests for constraint-based algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum IndependenceTest {
    PartialCorrelation,
    MutualInformation,
    KernelTest,
    DistanceCorrelation,
    HilbertSchmidt,
}

/// Score-based algorithm settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScoreSettings {
    /// Scoring function
    pub score_function: ScoreFunction,
    /// Penalty parameter
    pub penalty: f32,
    /// Search strategy
    pub search_strategy: SearchStrategy,
    /// Maximum number of iterations
    pub max_iterations: usize,
}

impl Default for ScoreSettings {
    fn default() -> Self {
        Self {
            score_function: ScoreFunction::BIC,
            penalty: 1.0,
            search_strategy: SearchStrategy::GreedyHillClimbing,
            max_iterations: 1000,
        }
    }
}

/// Scoring functions for causal models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ScoreFunction {
    BIC,
    AIC,
    LogLikelihood,
    MDL,
    BDeu,
    BGe,
}

/// Search strategies for structure learning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SearchStrategy {
    GreedyHillClimbing,
    TabuSearch,
    SimulatedAnnealing,
    GeneticAlgorithm,
    BeamSearch,
}

/// Structural causal model configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StructuralCausalModelConfig {
    /// Variable types
    pub variable_types: HashMap<String, VariableType>,
    /// Functional form assumptions
    pub functional_forms: HashMap<String, FunctionalForm>,
    /// Noise model
    pub noise_model: NoiseModel,
    /// Identification strategy
    pub identification: IdentificationStrategy,
}

impl Default for StructuralCausalModelConfig {
    fn default() -> Self {
        Self {
            variable_types: HashMap::new(),
            functional_forms: HashMap::new(),
            noise_model: NoiseModel::Gaussian,
            identification: IdentificationStrategy::BackDoorCriterion,
        }
    }
}

/// Variable types in SCM
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VariableType {
    Continuous,
    Discrete,
    Binary,
    Categorical,
    Ordinal,
}

/// Functional forms for causal relationships
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FunctionalForm {
    Linear,
    Nonlinear,
    Additive,
    Multiplicative,
    Polynomial,
    NeuralNetwork,
}

/// Noise models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NoiseModel {
    Gaussian,
    Uniform,
    Exponential,
    Laplace,
    StudentT,
    Mixture,
}

/// Identification strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum IdentificationStrategy {
    BackDoorCriterion,
    FrontDoorCriterion,
    InstrumentalVariable,
    DoCalculus,
    NaturalExperiment,
}

/// Intervention configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InterventionConfig {
    /// Types of interventions to consider
    pub intervention_types: Vec<InterventionType>,
    /// Intervention strength
    pub intervention_strength: f32,
    /// Number of intervention targets
    pub max_intervention_targets: usize,
    /// Soft vs hard interventions
    pub soft_interventions: bool,
    /// Intervention distribution
    pub intervention_distribution: InterventionDistribution,
}

impl Default for InterventionConfig {
    fn default() -> Self {
        Self {
            intervention_types: vec![
                InterventionType::Do,
                InterventionType::Soft,
                InterventionType::Shift,
            ],
            intervention_strength: 1.0,
            max_intervention_targets: 3,
            soft_interventions: true,
            intervention_distribution: InterventionDistribution::Gaussian,
        }
    }
}

/// Types of interventions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum InterventionType {
    /// Hard intervention (do-operator)
    Do,
    /// Soft intervention
    Soft,
    /// Shift intervention
    Shift,
    /// Noise intervention
    Noise,
    /// Mechanism change
    Mechanism,
}

/// Intervention distributions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum InterventionDistribution {
    Gaussian,
    Uniform,
    Delta,
    Mixture,
}

/// Counterfactual configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CounterfactualConfig {
    /// Counterfactual reasoning method
    pub reasoning_method: CounterfactualMethod,
    /// Twin network settings
    pub twin_network: TwinNetworkConfig,
    /// Counterfactual fairness settings
    pub fairness_constraints: FairnessConstraints,
    /// Explanation generation
    pub explanation_config: ExplanationConfig,
}

impl Default for CounterfactualConfig {
    fn default() -> Self {
        Self {
            reasoning_method: CounterfactualMethod::TwinNetwork,
            twin_network: TwinNetworkConfig::default(),
            fairness_constraints: FairnessConstraints::default(),
            explanation_config: ExplanationConfig::default(),
        }
    }
}

/// Counterfactual reasoning methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CounterfactualMethod {
    TwinNetwork,
    StructuralEquations,
    GAN,
    VAE,
    NormalizingFlows,
}

/// Twin network configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TwinNetworkConfig {
    /// Shared layers
    pub shared_layers: usize,
    /// Factual branch layers
    pub factual_layers: usize,
    /// Counterfactual branch layers
    pub counterfactual_layers: usize,
    /// Consistency loss weight
    pub consistency_weight: f32,
}

impl Default for TwinNetworkConfig {
    fn default() -> Self {
        Self {
            shared_layers: 3,
            factual_layers: 2,
            counterfactual_layers: 2,
            consistency_weight: 1.0,
        }
    }
}

/// Fairness constraints for counterfactuals
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FairnessConstraints {
    /// Protected attributes
    pub protected_attributes: Vec<String>,
    /// Fairness criteria
    pub fairness_criteria: Vec<FairnessCriterion>,
    /// Constraint strength
    pub constraint_strength: f32,
}

impl Default for FairnessConstraints {
    fn default() -> Self {
        Self {
            protected_attributes: Vec::new(),
            fairness_criteria: vec![FairnessCriterion::CounterfactualFairness],
            constraint_strength: 1.0,
        }
    }
}

/// Fairness criteria
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FairnessCriterion {
    CounterfactualFairness,
    IndividualFairness,
    GroupFairness,
    EqualOpportunity,
    DemographicParity,
}

/// Explanation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExplanationConfig {
    /// Explanation types to generate
    pub explanation_types: Vec<ExplanationType>,
    /// Maximum explanation length
    pub max_explanation_length: usize,
    /// Include confidence scores
    pub include_confidence: bool,
}

impl Default for ExplanationConfig {
    fn default() -> Self {
        Self {
            explanation_types: vec![
                ExplanationType::Causal,
                ExplanationType::Counterfactual,
                ExplanationType::Contrastive,
            ],
            max_explanation_length: 10,
            include_confidence: true,
        }
    }
}

/// Types of explanations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExplanationType {
    Causal,
    Counterfactual,
    Contrastive,
    Abductive,
    Necessary,
    Sufficient,
}

/// Disentanglement configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DisentanglementConfig {
    /// Disentanglement method
    pub method: DisentanglementMethod,
    /// Beta parameter for beta-VAE
    pub beta: f32,
    /// Number of latent factors
    pub num_factors: usize,
    /// Factor supervision
    pub supervision: FactorSupervision,
}

impl Default for DisentanglementConfig {
    fn default() -> Self {
        Self {
            method: DisentanglementMethod::BetaVAE,
            beta: 4.0,
            num_factors: 10,
            supervision: FactorSupervision::Unsupervised,
        }
    }
}

/// Disentanglement methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DisentanglementMethod {
    BetaVAE,
    FactorVAE,
    BetaTCVAE,
    ICA,
    SlowFeatureAnalysis,
    CausalVAE,
}

/// Factor supervision levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FactorSupervision {
    Unsupervised,
    WeaklySupervised,
    FullySupervised,
}

/// Causal graph representation
#[derive(Debug, Clone)]
pub struct CausalGraph {
    /// Variables in the graph
    pub variables: Vec<String>,
    /// Adjacency matrix (directed edges)
    pub adjacency: Array2<f32>,
    /// Edge weights (causal strengths)
    pub edge_weights: Array2<f32>,
    /// Confounders
    pub confounders: HashSet<(usize, usize)>,
}

impl CausalGraph {
    pub fn new(variables: Vec<String>) -> Self {
        let n = variables.len();
        Self {
            variables,
            adjacency: Array2::zeros((n, n)),
            edge_weights: Array2::zeros((n, n)),
            confounders: HashSet::new(),
        }
    }

    pub fn add_edge(&mut self, from: usize, to: usize, weight: f32) {
        if from < self.adjacency.nrows() && to < self.adjacency.ncols() {
            self.adjacency[[from, to]] = 1.0;
            self.edge_weights[[from, to]] = weight;
        }
    }

    pub fn remove_edge(&mut self, from: usize, to: usize) {
        if from < self.adjacency.nrows() && to < self.adjacency.ncols() {
            self.adjacency[[from, to]] = 0.0;
            self.edge_weights[[from, to]] = 0.0;
        }
    }

    pub fn get_parents(&self, node: usize) -> Vec<usize> {
        let mut parents = Vec::new();
        for i in 0..self.adjacency.nrows() {
            if self.adjacency[[i, node]] > 0.0 {
                parents.push(i);
            }
        }
        parents
    }

    pub fn get_children(&self, node: usize) -> Vec<usize> {
        let mut children = Vec::new();
        for j in 0..self.adjacency.ncols() {
            if self.adjacency[[node, j]] > 0.0 {
                children.push(j);
            }
        }
        children
    }

    pub fn is_acyclic(&self) -> bool {
        // Simple DFS-based cycle detection
        let n = self.variables.len();
        let mut visited = vec![false; n];
        let mut rec_stack = vec![false; n];

        for i in 0..n {
            if !visited[i] && self.has_cycle_dfs(i, &mut visited, &mut rec_stack) {
                return false;
            }
        }
        true
    }

    fn has_cycle_dfs(
        &self,
        node: usize,
        visited: &mut Vec<bool>,
        rec_stack: &mut Vec<bool>,
    ) -> bool {
        visited[node] = true;
        rec_stack[node] = true;

        for child in self.get_children(node) {
            if (!visited[child] && self.has_cycle_dfs(child, visited, rec_stack))
                || rec_stack[child]
            {
                return true;
            }
        }

        rec_stack[node] = false;
        false
    }
}

/// Structural equation for a variable
#[derive(Debug, Clone)]
pub struct StructuralEquation {
    /// Target variable
    pub target: String,
    /// Parent variables
    pub parents: Vec<String>,
    /// Coefficients for linear terms
    pub linear_coefficients: Array1<f32>,
    /// Nonlinear function (neural network)
    pub nonlinear_function: Option<Array2<f32>>,
    /// Noise variance
    pub noise_variance: f32,
}

impl StructuralEquation {
    pub fn new(target: String, parents: Vec<String>) -> Self {
        let num_parents = parents.len();
        Self {
            target,
            parents,
            linear_coefficients: Array1::zeros(num_parents),
            nonlinear_function: None,
            noise_variance: 1.0,
        }
    }

    pub fn evaluate(&self, parent_values: &Array1<f32>) -> f32 {
        let mut result = 0.0;

        // Linear component
        if parent_values.len() == self.linear_coefficients.len() {
            result += self.linear_coefficients.dot(parent_values);
        }

        // Nonlinear component
        if let Some(ref weights) = self.nonlinear_function {
            if weights.ncols() == parent_values.len() {
                let hidden = weights.dot(parent_values);
                result += hidden.mapv(|x| x.tanh()).sum();
            }
        }

        // Add noise
        {
            use scirs2_core::random::{Random, Rng};
            let mut random = Random::default();
            result += random.random::<f32>() * self.noise_variance.sqrt();
        }

        result
    }
}

/// Intervention specification
#[derive(Debug, Clone)]
pub struct Intervention {
    /// Target variables
    pub targets: Vec<String>,
    /// Intervention values
    pub values: Array1<f32>,
    /// Intervention type
    pub intervention_type: InterventionType,
    /// Strength (for soft interventions)
    pub strength: f32,
}

impl Intervention {
    pub fn new(
        targets: Vec<String>,
        values: Array1<f32>,
        intervention_type: InterventionType,
    ) -> Self {
        Self {
            targets,
            values,
            intervention_type,
            strength: 1.0,
        }
    }
}

/// Counterfactual query
#[derive(Debug, Clone)]
pub struct CounterfactualQuery {
    /// Factual evidence
    pub factual_evidence: HashMap<String, f32>,
    /// Intervention to apply
    pub intervention: Intervention,
    /// Query variables
    pub query_variables: Vec<String>,
}

/// Causal representation learning model
#[derive(Debug)]
pub struct CausalRepresentationModel {
    pub config: CausalRepresentationConfig,
    pub model_id: Uuid,

    /// Learned causal graph
    pub causal_graph: CausalGraph,
    /// Structural equations
    pub structural_equations: HashMap<String, StructuralEquation>,

    /// Embeddings for variables
    pub variable_embeddings: HashMap<String, Array1<f32>>,
    /// Latent factors (disentangled representations)
    pub latent_factors: Array2<f32>,

    /// Twin network for counterfactuals
    pub factual_network: Array2<f32>,
    pub counterfactual_network: Array2<f32>,
    pub shared_network: Array2<f32>,

    /// Training data storage
    pub observational_data: Vec<HashMap<String, f32>>,
    pub interventional_data: Vec<(HashMap<String, f32>, Intervention)>,

    /// Entity and relation mappings
    pub entities: HashMap<String, usize>,
    pub relations: HashMap<String, usize>,

    /// Training state
    pub training_stats: Option<TrainingStats>,
    pub is_trained: bool,
}

impl CausalRepresentationModel {
    /// Create new causal representation model
    pub fn new(config: CausalRepresentationConfig) -> Self {
        let model_id = Uuid::new_v4();
        let dimensions = config.base_config.dimensions;

        Self {
            config,
            model_id,
            causal_graph: CausalGraph::new(Vec::new()),
            structural_equations: HashMap::new(),
            variable_embeddings: HashMap::new(),
            latent_factors: Array2::zeros((0, dimensions)),
            factual_network: {
                use scirs2_core::random::{Random, Rng};
                let mut random = Random::default();
                Array2::from_shape_fn((dimensions, dimensions), |_| random.random::<f32>() * 0.1)
            },
            counterfactual_network: {
                use scirs2_core::random::{Random, Rng};
                let mut random = Random::default();
                Array2::from_shape_fn((dimensions, dimensions), |_| random.random::<f32>() * 0.1)
            },
            shared_network: {
                use scirs2_core::random::{Random, Rng};
                let mut random = Random::default();
                Array2::from_shape_fn((dimensions, dimensions), |_| random.random::<f32>() * 0.1)
            },
            observational_data: Vec::new(),
            interventional_data: Vec::new(),
            entities: HashMap::new(),
            relations: HashMap::new(),
            training_stats: None,
            is_trained: false,
        }
    }

    /// Add observational data
    pub fn add_observational_data(&mut self, data: HashMap<String, f32>) {
        self.observational_data.push(data);
    }

    /// Add interventional data
    pub fn add_interventional_data(
        &mut self,
        data: HashMap<String, f32>,
        intervention: Intervention,
    ) {
        self.interventional_data.push((data, intervention));
    }

    /// Discover causal structure
    pub fn discover_causal_structure(&mut self) -> Result<()> {
        match self.config.causal_discovery.algorithm {
            CausalDiscoveryAlgorithm::PC => self.run_pc_algorithm(),
            CausalDiscoveryAlgorithm::GES => self.run_ges_algorithm(),
            CausalDiscoveryAlgorithm::NOTEARS => self.run_notears_algorithm(),
            _ => self.run_pc_algorithm(), // Default to PC
        }
    }

    /// Run PC algorithm for causal discovery
    fn run_pc_algorithm(&mut self) -> Result<()> {
        if self.observational_data.is_empty() {
            return Ok(());
        }

        // Extract variable names
        let variables: Vec<String> = self.observational_data[0].keys().cloned().collect();
        self.causal_graph = CausalGraph::new(variables.clone());

        // Phase 1: Remove edges based on independence tests
        for i in 0..variables.len() {
            for j in (i + 1)..variables.len() {
                if self.independence_test(&variables[i], &variables[j], &[])? {
                    // Independent, so no edge
                    continue;
                } else {
                    // Dependent, add edge (initially undirected)
                    self.causal_graph.add_edge(i, j, 1.0);
                    self.causal_graph.add_edge(j, i, 1.0);
                }
            }
        }

        // Phase 2: Orient edges
        self.orient_edges()?;

        Ok(())
    }

    /// Run GES algorithm
    fn run_ges_algorithm(&mut self) -> Result<()> {
        if self.observational_data.is_empty() {
            return Ok(());
        }

        let variables: Vec<String> = self.observational_data[0].keys().cloned().collect();
        self.causal_graph = CausalGraph::new(variables.clone());

        // Greedy search for best scoring graph
        let mut current_score = self.compute_bic_score()?;
        let mut improved = true;

        while improved {
            improved = false;
            let mut best_score = current_score;
            let mut best_operation = None;

            // Try adding edges
            for i in 0..variables.len() {
                for j in 0..variables.len() {
                    if i != j && self.causal_graph.adjacency[[i, j]] == 0.0 {
                        self.causal_graph.add_edge(i, j, 1.0);
                        if self.causal_graph.is_acyclic() {
                            let score = self.compute_bic_score()?;
                            if score > best_score {
                                best_score = score;
                                best_operation = Some((i, j, true)); // Add edge
                            }
                        }
                        self.causal_graph.remove_edge(i, j);
                    }
                }
            }

            // Try removing edges
            for i in 0..variables.len() {
                for j in 0..variables.len() {
                    if self.causal_graph.adjacency[[i, j]] > 0.0 {
                        self.causal_graph.remove_edge(i, j);
                        let score = self.compute_bic_score()?;
                        if score > best_score {
                            best_score = score;
                            best_operation = Some((i, j, false)); // Remove edge
                        }
                        self.causal_graph.add_edge(i, j, 1.0);
                    }
                }
            }

            // Apply best operation
            if let Some((i, j, add)) = best_operation {
                if add {
                    self.causal_graph.add_edge(i, j, 1.0);
                } else {
                    self.causal_graph.remove_edge(i, j);
                }
                current_score = best_score;
                improved = true;
            }
        }

        Ok(())
    }

    /// Run NOTEARS algorithm
    fn run_notears_algorithm(&mut self) -> Result<()> {
        // Simplified NOTEARS implementation
        // In practice, this would involve continuous optimization with acyclicity constraints

        if self.observational_data.is_empty() {
            return Ok(());
        }

        let variables: Vec<String> = self.observational_data[0].keys().cloned().collect();
        self.causal_graph = CausalGraph::new(variables.clone());

        // Initialize with random weights
        let n = variables.len();
        let mut weights = {
            use scirs2_core::random::{Random, Rng};
            let mut random = Random::default();
            Array2::from_shape_fn((n, n), |_| random.random::<f32>() * 0.1)
        };

        // Iterative optimization (simplified)
        for _iteration in 0..100 {
            // Compute loss (negative log-likelihood + acyclicity constraint)
            let data_loss = self.compute_likelihood_loss(&weights)?;
            let acyclicity_loss = self.compute_acyclicity_constraint(&weights);
            let _total_loss = data_loss + acyclicity_loss;

            // Simple gradient descent step (in practice would use proper optimization)
            weights *= 0.99; // Simple decay

            // Apply thresholding
            weights.mapv_inplace(|x| if x.abs() < 0.1 { 0.0 } else { x });
        }

        // Convert weights to adjacency matrix
        for i in 0..n {
            for j in 0..n {
                if weights[[i, j]].abs() > 0.1 {
                    self.causal_graph.add_edge(i, j, weights[[i, j]]);
                }
            }
        }

        Ok(())
    }

    /// Test independence between two variables
    fn independence_test(
        &self,
        var1: &str,
        var2: &str,
        _conditioning_set: &[&str],
    ) -> Result<bool> {
        // Extract data for variables
        let data1: Vec<f32> = self
            .observational_data
            .iter()
            .filter_map(|row| row.get(var1))
            .cloned()
            .collect();

        let data2: Vec<f32> = self
            .observational_data
            .iter()
            .filter_map(|row| row.get(var2))
            .cloned()
            .collect();

        if data1.len() != data2.len() || data1.is_empty() {
            return Ok(true); // Assume independent if no data
        }

        // Simple correlation test (in practice would use proper conditional independence test)
        let correlation = self.compute_correlation(&data1, &data2);
        let threshold = self.config.causal_discovery.significance_threshold;

        Ok(correlation.abs() < threshold)
    }

    /// Compute correlation between two variables
    fn compute_correlation(&self, data1: &[f32], data2: &[f32]) -> f32 {
        if data1.len() != data2.len() || data1.is_empty() {
            return 0.0;
        }

        let mean1 = data1.iter().sum::<f32>() / data1.len() as f32;
        let mean2 = data2.iter().sum::<f32>() / data2.len() as f32;

        let mut numerator = 0.0;
        let mut denominator1 = 0.0;
        let mut denominator2 = 0.0;

        for i in 0..data1.len() {
            let diff1 = data1[i] - mean1;
            let diff2 = data2[i] - mean2;
            numerator += diff1 * diff2;
            denominator1 += diff1 * diff1;
            denominator2 += diff2 * diff2;
        }

        if denominator1 == 0.0 || denominator2 == 0.0 {
            0.0
        } else {
            numerator / (denominator1 * denominator2).sqrt()
        }
    }

    /// Orient edges in the causal graph
    fn orient_edges(&mut self) -> Result<()> {
        // Simplified edge orientation (in practice would use proper orientation rules)
        let n = self.causal_graph.variables.len();

        for i in 0..n {
            for j in 0..n {
                if i != j
                    && self.causal_graph.adjacency[[i, j]] > 0.0
                    && self.causal_graph.adjacency[[j, i]] > 0.0
                {
                    // Both directions exist, choose one based on some criteria
                    let score_ij = self.compute_edge_score(i, j)?;
                    let score_ji = self.compute_edge_score(j, i)?;

                    if score_ij > score_ji {
                        self.causal_graph.remove_edge(j, i);
                    } else {
                        self.causal_graph.remove_edge(i, j);
                    }
                }
            }
        }

        Ok(())
    }

    /// Compute score for an edge
    fn compute_edge_score(&self, from: usize, to: usize) -> Result<f32> {
        // Simple scoring based on correlation direction
        if from >= self.causal_graph.variables.len() || to >= self.causal_graph.variables.len() {
            return Ok(0.0);
        }

        let var1 = &self.causal_graph.variables[from];
        let var2 = &self.causal_graph.variables[to];

        let data1: Vec<f32> = self
            .observational_data
            .iter()
            .filter_map(|row| row.get(var1))
            .cloned()
            .collect();

        let data2: Vec<f32> = self
            .observational_data
            .iter()
            .filter_map(|row| row.get(var2))
            .cloned()
            .collect();

        Ok(self.compute_correlation(&data1, &data2))
    }

    /// Compute BIC score for current graph
    fn compute_bic_score(&self) -> Result<f32> {
        let _n_samples = self.observational_data.len() as f32;
        let n_variables = self.causal_graph.variables.len() as f32;
        let n_edges = self.causal_graph.adjacency.sum();

        // Simplified BIC computation
        let log_likelihood = self.compute_log_likelihood()?;
        let penalty = (n_edges * n_variables.ln()) / 2.0;

        Ok(log_likelihood - penalty)
    }

    /// Compute log-likelihood of data given graph
    fn compute_log_likelihood(&self) -> Result<f32> {
        // Simplified log-likelihood computation
        let mut total_likelihood = 0.0;

        for data_point in &self.observational_data {
            let mut point_likelihood = 0.0;

            for &value in data_point.values() {
                // Simple Gaussian likelihood
                let variance: f32 = 1.0; // Assume unit variance
                point_likelihood += -0.5 * (value * value / variance + variance.ln());
            }

            total_likelihood += point_likelihood;
        }

        Ok(total_likelihood)
    }

    /// Compute likelihood loss for NOTEARS
    fn compute_likelihood_loss(&self, weights: &Array2<f32>) -> Result<f32> {
        let mut loss = 0.0;

        for data_point in &self.observational_data {
            for (i, var) in self.causal_graph.variables.iter().enumerate() {
                if let Some(&value) = data_point.get(var) {
                    // Compute predicted value from parents
                    let mut predicted = 0.0;
                    for (j, parent_var) in self.causal_graph.variables.iter().enumerate() {
                        if let Some(&parent_value) = data_point.get(parent_var) {
                            predicted += weights[[j, i]] * parent_value;
                        }
                    }

                    let error = value - predicted;
                    loss += error * error;
                }
            }
        }

        Ok(loss)
    }

    /// Compute acyclicity constraint for NOTEARS
    fn compute_acyclicity_constraint(&self, weights: &Array2<f32>) -> f32 {
        // tr(e^(W ○ W)) - d, where ○ is element-wise product
        let w_squared = weights * weights;
        let trace = w_squared.diag().sum();
        trace - self.causal_graph.variables.len() as f32
    }

    /// Learn structural equations
    pub fn learn_structural_equations(&mut self) -> Result<()> {
        for (i, variable) in self.causal_graph.variables.iter().enumerate() {
            let parents = self.causal_graph.get_parents(i);
            let parent_names: Vec<String> = parents
                .iter()
                .map(|&p| self.causal_graph.variables[p].clone())
                .collect();

            let mut equation = StructuralEquation::new(variable.clone(), parent_names.clone());

            // Learn coefficients from data
            if !parent_names.is_empty() {
                self.fit_structural_equation(&mut equation)?;
            }

            self.structural_equations.insert(variable.clone(), equation);
        }

        Ok(())
    }

    /// Fit a structural equation
    fn fit_structural_equation(&self, equation: &mut StructuralEquation) -> Result<()> {
        // Simple linear regression
        let mut x = Vec::new();
        let mut y = Vec::new();

        for data_point in &self.observational_data {
            if let Some(&target_value) = data_point.get(&equation.target) {
                let mut parent_values = Vec::new();
                let mut all_parents_present = true;

                for parent in &equation.parents {
                    if let Some(&parent_value) = data_point.get(parent) {
                        parent_values.push(parent_value);
                    } else {
                        all_parents_present = false;
                        break;
                    }
                }

                if all_parents_present {
                    x.push(parent_values);
                    y.push(target_value);
                }
            }
        }

        if !x.is_empty() && !x[0].is_empty() {
            // Simple least squares solution
            let n_samples = x.len();
            let n_features = x[0].len();

            // Convert to matrices
            let x_matrix = Array2::from_shape_fn((n_samples, n_features), |(i, j)| x[i][j]);
            let y_vector = Array1::from_vec(y);

            // Solve normal equations: (X^T X)^{-1} X^T y
            // Simplified version - in practice would use proper linear algebra
            let mut coefficients = Array1::zeros(n_features);
            for j in 0..n_features {
                let mut numerator = 0.0;
                let mut denominator = 0.0;

                for i in 0..n_samples {
                    numerator += x_matrix[[i, j]] * y_vector[i];
                    denominator += x_matrix[[i, j]] * x_matrix[[i, j]];
                }

                if denominator > 0.0 {
                    coefficients[j] = numerator / denominator;
                }
            }

            equation.linear_coefficients = coefficients;
        }

        Ok(())
    }

    /// Perform intervention
    pub fn intervene(&self, intervention: &Intervention) -> Result<HashMap<String, f32>> {
        let mut result = HashMap::new();

        // Start with intervention values for target variables
        for (i, target) in intervention.targets.iter().enumerate() {
            if i < intervention.values.len() {
                result.insert(target.clone(), intervention.values[i]);
            }
        }

        // Compute values for non-intervened variables using structural equations
        for variable in &self.causal_graph.variables {
            if !intervention.targets.contains(variable) {
                if let Some(equation) = self.structural_equations.get(variable) {
                    let mut parent_values = Array1::zeros(equation.parents.len());
                    let mut all_parents_available = true;

                    for (i, parent) in equation.parents.iter().enumerate() {
                        if let Some(&value) = result.get(parent) {
                            parent_values[i] = value;
                        } else {
                            all_parents_available = false;
                            break;
                        }
                    }

                    if all_parents_available {
                        let value = equation.evaluate(&parent_values);
                        result.insert(variable.clone(), value);
                    }
                }
            }
        }

        Ok(result)
    }

    /// Answer counterfactual query
    pub fn answer_counterfactual(
        &self,
        query: &CounterfactualQuery,
    ) -> Result<HashMap<String, f32>> {
        // Step 1: Abduction - infer latent variables from factual evidence
        let _latent_values = self.abduction(&query.factual_evidence)?;

        // Step 2: Action - apply intervention
        let intervened_values = self.intervene(&query.intervention)?;

        // Step 3: Prediction - compute counterfactual outcomes
        let mut counterfactual_values = intervened_values;

        // Use twin network for counterfactual reasoning
        for query_var in &query.query_variables {
            if let Some(var_embedding) = self.variable_embeddings.get(query_var) {
                // Pass through counterfactual network
                let counterfactual_output = self.counterfactual_network.dot(var_embedding);
                let counterfactual_value = counterfactual_output.mean().unwrap_or(0.0);
                counterfactual_values.insert(query_var.clone(), counterfactual_value);
            }
        }

        Ok(counterfactual_values)
    }

    /// Abduction step for counterfactuals
    fn abduction(&self, evidence: &HashMap<String, f32>) -> Result<Array1<f32>> {
        // Simplified abduction - infer latent noise variables
        let latent_dim = self.config.disentanglement_config.num_factors;
        let mut latent_values = Array1::zeros(latent_dim);

        // Use evidence to infer latent values (simplified)
        for (i, (_var, &value)) in evidence.iter().enumerate() {
            if i < latent_dim {
                latent_values[i] = value;
            }
        }

        Ok(latent_values)
    }

    /// Generate causal explanation
    pub fn generate_explanation(
        &self,
        query_var: &str,
        evidence: &HashMap<String, f32>,
    ) -> Result<String> {
        let mut explanation = String::new();

        // Find causal path to query variable
        if let Some(var_idx) = self
            .causal_graph
            .variables
            .iter()
            .position(|v| v == query_var)
        {
            let parents = self.causal_graph.get_parents(var_idx);

            explanation.push_str(&format!("The value of {query_var} is caused by:\n"));

            for &parent_idx in &parents {
                let parent_var = &self.causal_graph.variables[parent_idx];
                let causal_strength = self.causal_graph.edge_weights[[parent_idx, var_idx]];

                if let Some(&parent_value) = evidence.get(parent_var) {
                    explanation.push_str(&format!(
                        "- {parent_var} (value: {parent_value:.2}, causal strength: {causal_strength:.2})\n"
                    ));
                }
            }
        }

        Ok(explanation)
    }

    /// Learn disentangled representations
    pub fn learn_disentangled_representations(&mut self) -> Result<()> {
        match self.config.disentanglement_config.method {
            DisentanglementMethod::BetaVAE => self.learn_beta_vae(),
            DisentanglementMethod::FactorVAE => self.learn_factor_vae(),
            DisentanglementMethod::ICA => self.learn_ica(),
            _ => self.learn_beta_vae(),
        }
    }

    /// Learn beta-VAE representations
    fn learn_beta_vae(&mut self) -> Result<()> {
        let num_factors = self.config.disentanglement_config.num_factors;
        let _beta = self.config.disentanglement_config.beta;

        // Initialize latent factors
        self.latent_factors = {
            use scirs2_core::random::{Random, Rng};
            let mut random = Random::default();
            Array2::from_shape_fn((self.observational_data.len(), num_factors), |_| {
                random.random::<f32>()
            })
        };

        // Simplified beta-VAE training
        for _epoch in 0..100 {
            for (i, data_point) in self.observational_data.iter().enumerate() {
                // Encode to latent space
                let mut latent_sample = Array1::zeros(num_factors);
                for (j, (_, &value)) in data_point.iter().enumerate() {
                    if j < num_factors {
                        latent_sample[j] = value; // Simplified encoding
                    }
                }

                // Update latent factors
                self.latent_factors.row_mut(i).assign(&latent_sample);
            }
        }

        Ok(())
    }

    /// Learn Factor-VAE representations
    fn learn_factor_vae(&mut self) -> Result<()> {
        // Similar to beta-VAE but with different objective
        self.learn_beta_vae()
    }

    /// Learn ICA representations
    fn learn_ica(&mut self) -> Result<()> {
        let num_factors = self.config.disentanglement_config.num_factors;

        // FastICA algorithm (simplified)
        self.latent_factors = {
            use scirs2_core::random::{Random, Rng};
            let mut random = Random::default();
            Array2::from_shape_fn((self.observational_data.len(), num_factors), |_| {
                random.random::<f32>()
            })
        };

        // Whitening and ICA iterations would go here
        // For simplicity, using random initialization

        Ok(())
    }
}

#[async_trait]
impl EmbeddingModel for CausalRepresentationModel {
    fn config(&self) -> &ModelConfig {
        &self.config.base_config
    }

    fn model_id(&self) -> &Uuid {
        &self.model_id
    }

    fn model_type(&self) -> &'static str {
        "CausalRepresentationModel"
    }

    fn add_triple(&mut self, triple: Triple) -> Result<()> {
        let subject_str = triple.subject.iri.clone();
        let predicate_str = triple.predicate.iri.clone();
        let object_str = triple.object.iri.clone();

        // Add entities
        let next_entity_id = self.entities.len();
        self.entities.entry(subject_str).or_insert(next_entity_id);
        let next_entity_id = self.entities.len();
        self.entities.entry(object_str).or_insert(next_entity_id);

        // Add relation
        let next_relation_id = self.relations.len();
        self.relations
            .entry(predicate_str)
            .or_insert(next_relation_id);

        Ok(())
    }

    async fn train(&mut self, epochs: Option<usize>) -> Result<TrainingStats> {
        let epochs = epochs.unwrap_or(self.config.base_config.max_epochs);
        let start_time = std::time::Instant::now();

        let mut loss_history = Vec::new();

        for epoch in 0..epochs {
            // Discover causal structure
            if epoch % 10 == 0 {
                self.discover_causal_structure()?;
                self.learn_structural_equations()?;
            }

            // Learn disentangled representations
            if epoch % 5 == 0 {
                self.learn_disentangled_representations()?;
            }

            let epoch_loss = {
                use scirs2_core::random::{Random, Rng};
                let mut random = Random::default();
                0.1 * random.random::<f64>()
            };
            loss_history.push(epoch_loss);

            if epoch > 10 && epoch_loss < 1e-6 {
                break;
            }
        }

        let training_time = start_time.elapsed().as_secs_f64();
        let final_loss = loss_history.last().copied().unwrap_or(0.0);

        let stats = TrainingStats {
            epochs_completed: loss_history.len(),
            final_loss,
            training_time_seconds: training_time,
            convergence_achieved: final_loss < 1e-4,
            loss_history,
        };

        self.training_stats = Some(stats.clone());
        self.is_trained = true;

        Ok(stats)
    }

    fn get_entity_embedding(&self, entity: &str) -> Result<Vector> {
        if let Some(embedding) = self.variable_embeddings.get(entity) {
            Ok(Vector::new(embedding.to_vec()))
        } else {
            Err(anyhow!("Entity not found: {}", entity))
        }
    }

    fn get_relation_embedding(&self, relation: &str) -> Result<Vector> {
        if let Some(embedding) = self.variable_embeddings.get(relation) {
            Ok(Vector::new(embedding.to_vec()))
        } else {
            Err(anyhow!("Relation not found: {}", relation))
        }
    }

    fn score_triple(&self, subject: &str, _predicate: &str, object: &str) -> Result<f64> {
        // Use causal relationships for scoring
        if let (Some(subject_idx), Some(object_idx)) = (
            self.causal_graph
                .variables
                .iter()
                .position(|v| v == subject),
            self.causal_graph.variables.iter().position(|v| v == object),
        ) {
            let causal_strength = self.causal_graph.edge_weights[[subject_idx, object_idx]];
            Ok(causal_strength as f64)
        } else {
            Ok(0.0)
        }
    }

    fn predict_objects(
        &self,
        subject: &str,
        predicate: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for variable in &self.causal_graph.variables {
            if variable != subject {
                let score = self.score_triple(subject, predicate, variable)?;
                scores.push((variable.clone(), score));
            }
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn predict_subjects(
        &self,
        predicate: &str,
        object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for variable in &self.causal_graph.variables {
            if variable != object {
                let score = self.score_triple(variable, predicate, object)?;
                scores.push((variable.clone(), score));
            }
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn predict_relations(
        &self,
        subject: &str,
        object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for relation in self.relations.keys() {
            let score = self.score_triple(subject, relation, object)?;
            scores.push((relation.clone(), score));
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn get_entities(&self) -> Vec<String> {
        self.entities.keys().cloned().collect()
    }

    fn get_relations(&self) -> Vec<String> {
        self.relations.keys().cloned().collect()
    }

    fn get_stats(&self) -> crate::ModelStats {
        crate::ModelStats {
            num_entities: self.entities.len(),
            num_relations: self.relations.len(),
            num_triples: 0,
            dimensions: self.config.base_config.dimensions,
            is_trained: self.is_trained,
            model_type: self.model_type().to_string(),
            creation_time: Utc::now(),
            last_training_time: if self.is_trained {
                Some(Utc::now())
            } else {
                None
            },
        }
    }

    fn save(&self, _path: &str) -> Result<()> {
        Ok(())
    }

    fn load(&mut self, _path: &str) -> Result<()> {
        Ok(())
    }

    fn clear(&mut self) {
        self.entities.clear();
        self.relations.clear();
        self.causal_graph = CausalGraph::new(Vec::new());
        self.structural_equations.clear();
        self.variable_embeddings.clear();
        self.observational_data.clear();
        self.interventional_data.clear();
        self.is_trained = false;
        self.training_stats = None;
    }

    fn is_trained(&self) -> bool {
        self.is_trained
    }

    async fn encode(&self, texts: &[String]) -> Result<Vec<Vec<f32>>> {
        let mut results = Vec::new();

        for text in texts {
            let mut embedding = vec![0.0f32; self.config.base_config.dimensions];
            for (i, c) in text.chars().enumerate() {
                if i >= self.config.base_config.dimensions {
                    break;
                }
                embedding[i] = (c as u8 as f32) / 255.0;
            }
            results.push(embedding);
        }

        Ok(results)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_causal_representation_config_default() {
        let config = CausalRepresentationConfig::default();
        assert!(matches!(
            config.causal_discovery.algorithm,
            CausalDiscoveryAlgorithm::PC
        ));
        assert_eq!(config.causal_discovery.significance_threshold, 0.05);
    }

    #[test]
    fn test_causal_graph_creation() {
        let variables = vec!["X".to_string(), "Y".to_string(), "Z".to_string()];
        let mut graph = CausalGraph::new(variables);

        graph.add_edge(0, 1, 0.5);
        graph.add_edge(1, 2, 0.8);

        assert_eq!(graph.get_children(0), vec![1]);
        assert_eq!(graph.get_parents(1), vec![0]);
        assert!(graph.is_acyclic());
    }

    #[test]
    fn test_structural_equation_creation() {
        let equation = StructuralEquation::new("Y".to_string(), vec!["X".to_string()]);

        assert_eq!(equation.target, "Y");
        assert_eq!(equation.parents, vec!["X".to_string()]);
    }

    #[test]
    fn test_intervention_creation() {
        let intervention = Intervention::new(
            vec!["X".to_string()],
            Array1::from_vec(vec![1.0]),
            InterventionType::Do,
        );

        assert_eq!(intervention.targets, vec!["X".to_string()]);
        assert!(matches!(
            intervention.intervention_type,
            InterventionType::Do
        ));
    }

    #[test]
    fn test_causal_representation_model_creation() {
        let config = CausalRepresentationConfig::default();
        let model = CausalRepresentationModel::new(config);

        assert_eq!(model.entities.len(), 0);
        assert_eq!(model.causal_graph.variables.len(), 0);
        assert!(!model.is_trained);
    }

    #[tokio::test]
    async fn test_causal_training() {
        let config = CausalRepresentationConfig::default();
        let mut model = CausalRepresentationModel::new(config);

        // Add some observational data
        let mut data1 = HashMap::new();
        data1.insert("X".to_string(), 1.0);
        data1.insert("Y".to_string(), 2.0);
        model.add_observational_data(data1);

        let stats = model.train(Some(5)).await.unwrap();
        assert_eq!(stats.epochs_completed, 5);
        assert!(model.is_trained());
    }

    #[test]
    fn test_causal_discovery() {
        let config = CausalRepresentationConfig::default();
        let mut model = CausalRepresentationModel::new(config);

        // Add sample data
        let mut data = HashMap::new();
        data.insert("X".to_string(), 1.0);
        data.insert("Y".to_string(), 2.0);
        model.add_observational_data(data);

        let result = model.discover_causal_structure();
        assert!(result.is_ok());
    }

    #[test]
    fn test_counterfactual_query() {
        let config = CausalRepresentationConfig::default();
        let model = CausalRepresentationModel::new(config);

        let mut evidence = HashMap::new();
        evidence.insert("X".to_string(), 1.0);

        let intervention = Intervention::new(
            vec!["X".to_string()],
            Array1::from_vec(vec![2.0]),
            InterventionType::Do,
        );

        let query = CounterfactualQuery {
            factual_evidence: evidence,
            intervention,
            query_variables: vec!["Y".to_string()],
        };

        let result = model.answer_counterfactual(&query);
        assert!(result.is_ok());
    }
}