deep_causality_algorithms 0.3.0

Computational causality algorithms and utils used in the DeepCausality project.
Documentation

deep_causality_algorithms

Crates.io Docs.rs MIT licensed Tests

A collection of computational causality algorithms used in the DeepCausality project. This crate provides tools for analyzing and decomposing causal relationships in complex systems.

The cornerstone of this crate is surd_states, a high-performance Rust implementation of the SURD-states algorithm. Based on the paper "Observational causality by states and interaction type for scientific discovery" (Martínez-Sánchez and Lozano-Durán, 2025), this algorithm decomposes the mutual information between a set of source variables and a target variable into its fundamental components: Synergistic, Unique, and Redundant (SURD).

This decomposition allows for a deep, nuanced understanding of causal structures, moving beyond simple correlations to reveal the nature of multi-variable interactions.

Alongside SURD, the crate provides brcd, a Rust implementation of Bayesian Root Cause Discovery (BRCD) based on the paper "Root Cause Analysis of Failures in Microservices via Bayesian Root Cause Discovery" (Lee, Zhou, and Kocaoglu, 2026). SURD decomposes how a set of sources jointly influence a target; BRCD answers a different question. Given a normal dataset, an anomalous dataset, and a causal graph over the same variables, it identifies which variables most likely caused the anomaly. BRCD scores every candidate root-cause set with a Bayesian posterior and returns the candidates ranked from most to least probable.

Key Features

  • Faithful & Performant Implementation: A high-performance, mathematically faithful Rust port of the SURD-states algorithm, optimized for speed and memory efficiency.
  • Rich Causal Decomposition: Decomposes the total causal influence into:
    • Redundant (R): Overlapping information provided by multiple sources.
    • Unique (U): Information provided by a single source independently.
    • Synergistic (S): New information that emerges only from the combination of sources.
  • State-Dependent Analysis: Provides detailed state-dependent maps that reveal how causal influences change based on the system's current state.
  • Information Leak Quantification: Explicitly calculates the "information leak," which quantifies the influence of unobserved variables or inherent randomness in the system.
  • Robust Incomplete Data Handling (CDL Variant): The surd_states_cdl function provides a variant of the SURD-states algorithm specifically designed to gracefully manage missing or undefined probability values (None in CausalTensor<Option<f64>>). This is crucial for real-world datasets where data incompleteness is common, allowing for meaningful causal insights even with partial information by ignoring None values in calculations and propagating uncertainty.
  • Minimum Redundancy Maximum Relevance (mRMR) Feature Selection: Implements the mRMR algorithm to select features that are maximally relevant to a target variable and minimally redundant among themselves. The algorithm now returns a ranked list of features along with their normalized importance scores (between 0.0 and 1.0), providing a clear indication of each feature's contribution.
  • Bayesian Root Cause Discovery (BRCD): The brcd_run function localizes the root cause of an anomaly. Given two aligned datasets (a normal regime and an anomalous regime) and a CPDAG over the shared variables, it augments the graph with a soft-intervention F-node, scores each candidate root-cause set with a plug-in ridge-Gaussian (continuous) or Dirichlet (discrete) likelihood, and ranks the candidates by their posterior probability p(R | D). The ranking is computed on the log-posterior directly, so it stays stable when a single fault dominates.
  • Performance Optimized:
    • Algorithmic Capping: Use the MaxOrder enum to limit the analysis to a tractable number of interactions ( e.g., pairwise), reducing complexity from exponential O(2^N) to polynomial O(N^k).
    • Parallel Execution: When compiled with the parallel feature flag, the main decomposition loop of the SURD algorithm, the feature selection loops of the mRMR algorithm, and the per-family likelihood scoring of the BRCD algorithm run in parallel across all available CPU cores using rayon. The BRCD family scoring is the dominant cost, and each family is independent, so the parallel and sequential results are identical.

Installation

cargo add deep_causality_algorithms

Usage

The primary function is surd_states, which takes a CausalTensor representing a joint probability distribution and returns a SurdResult.

use deep_causality_algorithms::{surd_states, MaxOrder};
use deep_causality_data_structures::CausalTensor;

// Create a joint probability distribution for a target and 2 source variables.
// Shape: [target_states, source1_states, source2_states] = [2, 2, 2]
let data = vec![
    0.1, 0.2, // P(T=0, S1=0, S2=0), P(T=0, S1=0, S2=1)
    0.0, 0.2, // P(T=0, S1=1, S2=0), P(T=0, S1=1, S2=1)
    0.3, 0.0, // P(T=1, S1=0, S2=0), P(T=1, S1=0, S2=1)
    0.1, 0.1, // P(T=1, S1=1, S2=0), P(T=1, S1=1, S2=1)
];
let p_raw = CausalTensor::new(data, vec![2, 2, 2]).unwrap();

// Perform a full decomposition (k=N=2)
let full_result = surd_states(&p_raw, MaxOrder::Max).unwrap();

// Print the detailed decomposition
println!("{}", &full_result);

// Access specific results
println!("Information Leak: {:.3}", full_result.info_leak());

// Synergistic information for the pair of variables {1, 2}
if let Some(synergy) = full_result.synergistic_info().get(&vec![1, 2]) {
    println!("Synergistic Info for {{1, 2}}: {:.3}", synergy);
}

Handling Incomplete Data with surd_states_cdl

For datasets containing missing or incomplete information, the surd_states_cdl function provides a robust solution. It operates on CausalTensor<Option<f64>>, gracefully handling None values by ignoring them in calculations and propagating uncertainty, allowing for causal discovery even with partial data.

use deep_causality_algorithms::{surd_states_cdl, MaxOrder};
use deep_causality_data_structures::CausalTensor;

// Create a joint probability distribution with missing data (None values).
// Shape: [target_states, source1_states, source2_states] = [2, 2, 2]
let data_with_nones = vec![
    Some(0.1), Some(0.2), // P(T=0, S1=0, S2=0), P(T=0, S1=0, S2=1)
    None,      Some(0.2), // P(T=0, S1=1, S2=0) is missing, P(T=0, S1=1, S2=1)
    Some(0.3), None,      // P(T=1, S1=0, S2=0), P(T=1, S1=0, S2=1) is missing
    Some(0.1), Some(0.1), // P(T=1, S1=1, S2=0), P(T=1, S1=1, S2=1)
];
let p_raw_with_nones = CausalTensor::new(data_with_nones, vec![2, 2, 2]).unwrap();

// Perform a full decomposition with None handling
let full_result_cdl = surd_states_cdl(&p_raw_with_nones, MaxOrder::Max).unwrap();

// Print the detailed decomposition
println!("CDL Result: {}", &full_result_cdl);

// Access specific results
println!("CDL Information Leak: {:.3}", full_result_cdl.info_leak());

Minimum Redundancy Maximum Relevance (mRMR) Feature Selection

The mRMR algorithm is a powerful tool for selecting a subset of features that are maximally relevant to a target variable and minimally redundant among themselves. This helps in reducing dimensionality and focusing causal analysis on the most informative variables. This implementation follows the mRMR formulation of Zhao, Anand, and Wang (2019). It returns a ranked list of features along with their normalized importance scores (between 0.0 and 1.0).

use deep_causality_algorithms::mrmr::mrmr_features_selector;
use deep_causality_tensor::CausalTensor;

let data = vec![
    10.0, 12.0, 1.0, 11.0,
    20.0, 21.0, 5.0, 22.0,
    30.0, 33.0, 2.0, 31.0,
    40.0, 40.0, 8.0, 43.0,
    50.0, 55.0, 3.0, 52.0,
];
let mut tensor = CausalTensor::new(data, vec![5, 4]).unwrap();

// Select 2 features, with the target variable in column 3.
let selected_features_with_scores = mrmr_features_selector(&mut tensor, 2, 3).unwrap();

println!("Selected Features and Scores: {:?}", selected_features_with_scores);

A higher mRMR score (and thus a higher normalized importance score) indicates that the feature is not only highly relevant to the target but also provides new, non-redundant information compared to the features already chosen. It's a measure of a feature's unique and strong contribution to predicting the target within the context of the selected feature set.

Bayesian Root Cause Discovery (BRCD)

Where SURD and mRMR describe how variables influence a target, BRCD answers a different question: which variable most likely caused an observed anomaly? The brcd_run function takes a normal dataset, an anomalous dataset, a CPDAG over the shared variables, and a BrcdConfig. It returns a BrcdResult whose ranks() list the candidate root-cause sets from most to least probable, and whose top() returns the single most probable set.

use deep_causality_algorithms::brcd::{brcd_run, BrcdConfig};
use deep_causality_rand::{Distribution, Normal, Xoshiro256};
use deep_causality_tensor::CausalTensor;
use deep_causality_topology::MixedGraph;

// A linear-Gaussian chain X -> Y -> Z. `y_intercept` shifts Y's own mechanism.
fn chain(n: usize, y_intercept: f64, seed: u64) -> CausalTensor<f64> {
    let mut rng = Xoshiro256::from_seed(seed);
    let noise = Normal::new(0.0_f64, 1.0).unwrap();
    let mut data = Vec::with_capacity(n * 3);
    for _ in 0..n {
        let x = noise.sample(&mut rng);
        let y = y_intercept + 1.5 * x + noise.sample(&mut rng);
        let z = 2.0 * y + noise.sample(&mut rng);
        data.extend_from_slice(&[x, y, z]);
    }
    CausalTensor::new(data, vec![n, 3]).unwrap()
}

// Two aligned regimes over the variables [X, Y, Z]. Only Y's mechanism changes
// between them (its intercept jumps), so Y is the true root cause.
let normal = chain(120, 0.0, 1);
let anomalous = chain(120, 4.0, 2);

// The CPDAG over the three variables: the undirected chain X — Y — Z.
let unit = CausalTensor::new(vec![(); 3], vec![3]).unwrap();
let mut cpdag = MixedGraph::new(3, unit, 0).unwrap();
cpdag.add_undirected(0, 1).unwrap();
cpdag.add_undirected(1, 2).unwrap();

// Run BRCD with the continuous (ridge-Gaussian) family, seed 7, single cause.
let result = brcd_run(&normal, &anomalous, &cpdag, &BrcdConfig::continuous(7)).unwrap();

println!("{result}");
println!("Top root cause: {:?}", result.top()); // Some([1]) = Y

BrcdConfig::continuous(seed) selects the ridge-Gaussian family for continuous data; BrcdConfig::discrete(seed) selects the Dirichlet family for categorical data. The num_root_causes field sets how many simultaneous root causes a candidate set holds (k).

From Discovery to Model: Connecting SURD to DeepCausality

The surd_states algorithm serves as a bridge from observational data to executable causal models with the DeepCausality.

1. Mapping Causal Links to CausaloidGraph Structure

The aggregate SURD results inform the structure of the CausaloidGraph.

  • A strong unique influence from S1 to T suggests a direct edge: Causaloid(S1) -> Causaloid(T).
  • A strong synergistic influence from S1 and S2 onto T suggests a many-to-one connection where Causaloid(S1) and Causaloid(S2) both point to Causaloid(T).
  • A high information leak suggests that the Causaloid for T should model a high degree of internal randomness or dependency on an unobserved Context.

2. Mapping State-Dependency to Causaloid Logic

The state-dependent maps provide the exact conditional logic for a Causaloid's causal_fn. For example, if SURD shows that S1's influence on T is strong only when S1 > 0, this condition can be programmed directly into the Causaloid.

3. Modeling Multiple Causes with CausaloidCollection

SURD's ability to detect multi-causal relationships is perfectly complemented by the CausaloidCollection, which models the interplay of multiple factors. The SURD results guide the choice of the collection's AggregateLogic:

  • Strong SYNERGY (e.g., A and B are required for C) maps to AggregateLogic::All (Conjunction).
  • Strong UNIQUE or REDUNDANT influences (e.g., A or B can cause C) maps to AggregateLogic::Any (Disjunction).
  • Complex mixed influences (e.g., any two of three factors cause C) maps to AggregateLogic::Some(k) (Threshold).

In summary, surd_states provides the data-driven evidence to identify multi-causal structures, and the DeepCausality primitives provide the formal mechanisms to build an executable model of that precise structure.

Example: Decomposing Causal Structure

The crate includes a detailed example (example_surd) that demonstrates how to use the surd_states algorithm and, more importantly, how to interpret its rich output. It runs through several test cases with different underlying causal structures (e.g., synergistic, noisy, random) and explains what each part of the output means.

To run the example:

cargo run --example example_surd

For a detailed walkthrough of the output, see the example's README.

References

The algorithms in this crate are Rust implementations of the following published work. Credit for the methods belongs to their original authors.

  • SURD — Álvaro Martínez-Sánchez and Adrián Lozano-Durán. "Observational causality by states and interaction type for scientific discovery." arXiv:2505.10878 (2025). https://arxiv.org/abs/2505.10878
  • mRMR — Zhenyu Zhao, Radhika Anand, and Mallory Wang. "Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform." arXiv:1908.05376 (2019). https://arxiv.org/abs/1908.05376
  • BRCD — Kenneth Lee, Zihan Zhou, and Murat Kocaoglu. "Root Cause Analysis of Failures in Microservices via Bayesian Root Cause Discovery." International Conference on Machine Learning (ICML), 2026. https://icml.cc/virtual/2026/poster/65359

👨‍💻👩‍💻 Contribution

Contributions are welcomed especially related to documentation, example code, and fixes. If unsure where to start, just open an issue and ask.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in deep_causality by you, shall be licensed under the MIT licence, without any additional terms or conditions.

📜 Licence

This project is licensed under the MIT license.

👮️ Security

For details about security, please read the security policy.

💻 Author

  • Marvin Hansen.
  • Github GPG key ID: 369D5A0B210D39BC
  • GPG Fingerprint: 4B18 F7B2 04B9 7A72 967E 663E 369D 5A0B 210D 39BC