Local Causal Structure Learning in the Presence of Latent Variables

Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng·May 25, 2024

Summary

The paper investigates local causal structure learning in scenarios with latent variables, proposing the MMB-by-MMB algorithm. This method identifies direct causes and effects of a target variable without causal sufficiency assumptions, relying on m-separation and V-structures. The algorithm is theoretically sound under Markov and faithfulness conditions and is shown to be effective and efficient through experiments on synthetic and real-world data. It compares favorably to existing LCS methods, especially in practical applications. The study also discusses related work, algorithmic details, and performance comparisons, demonstrating MMB-by-MMB's advantages in handling latent confounders and outperforming competitors in terms of precision, recall, and F1 score.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of discovering causal relationships from observational data, specifically in the presence of latent variables . This problem is not entirely new, as there have been existing methods and algorithms developed to recover causal structures among observed variables in the presence of latent variables . The paper focuses on locally identifying potential parents and children of a target variable from observational data that may include latent variables, bridging the gap between global and local structure learning . The research delves into deriving theoretical consistency results and presenting a principled method for determining direct cause or effect relationships under certain conditions, contributing to the field of causal inference and local causal structure learning .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to local causal structure learning in the presence of latent variables . The primary focus is on identifying potential parents and children of a target variable from observational data that may include latent variables . The paper delves into deriving theoretical consistency results by utilizing causal information from m-separation and V-structures, bridging the gap between global and local structure learning . The goal is to determine whether a variable is a direct cause or effect of a target by developing a principled method based on causal Markov and faithfulness conditions . The approach presented in the paper is theoretically validated under standard causal conditions with infinite samples, and its effectiveness and efficiency are confirmed through experimental results on synthetic and real-world data .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper introduces a novel local causal discovery algorithm called MMB-by-MMB, specifically designed to effectively identify causal structures in models with latent variables . This algorithm focuses on learning local causal relationships rather than the global causal structure, which is often more relevant in real-world scenarios . Unlike existing global algorithms, the MMB-by-MMB method demonstrates the capability to identify causal structures under equivalent identification conditions with significantly lower computational expense . Additionally, the paper provides proof validating the correctness of the MMB-by-MMB algorithm .

Furthermore, the study acknowledges the challenges posed by latent variables in determining causes and effects solely from observational data without additional assumptions. To address this, the paper suggests exploring the utilization of background knowledge, such as leveraging data generation mechanisms or expert knowledge, to further aid in identifying causes and effects within local structures, which remains a future research direction . This approach aligns with the need to enhance causal discovery methods by incorporating external information to improve the accuracy of causal relationships . The MMB-by-MMB algorithm proposed in the paper offers several key characteristics and advantages compared to previous methods in local causal structure learning .

  1. Efficiency and Computational Cost: The MMB-by-MMB algorithm is designed to identify causal structures in models with latent variables with significantly lower computational expense compared to existing global algorithms . This efficiency is crucial for practical applications where computational resources are limited.

  2. Local Causal Discovery: Unlike global algorithms that focus on learning the entire causal graph, the MMB-by-MMB method concentrates on local causal relationships, specifically targeting the direct causes and effects of a target variable based on the estimated local structure . This local approach is beneficial in scenarios where understanding the causal relationships surrounding a single target variable is more relevant.

  3. Validation and Correctness: The paper provides proof validating the correctness of the MMB-by-MMB algorithm, ensuring its reliability and accuracy in identifying causal structures in the presence of latent variables .

  4. Future Research Directions: Acknowledging the challenges posed by latent variables in causal discovery, the paper suggests exploring the utilization of background knowledge, such as leveraging data generation mechanisms or expert knowledge, to further aid in identifying causes and effects within local structures . This highlights a future research direction to enhance causal discovery methods by incorporating external information for improved accuracy.

  5. Experimental Efficacy: Through extensive experiments, the paper demonstrates the efficacy of the MMB-by-MMB algorithm on both benchmark network structures and real-world data, showcasing its practical utility and performance .

In summary, the MMB-by-MMB algorithm stands out for its efficiency, focus on local causal relationships, validation of correctness, potential for leveraging background knowledge, and demonstrated efficacy in experimental settings, offering a promising approach for causal structure learning in the presence of latent variables.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of local causal structure learning in the presence of latent variables. Noteworthy researchers in this area include Spirtes, Glymour, and Scheines , who have contributed to the understanding of causation, prediction, and search. Additionally, Tsamardinos and Aliferis have worked towards principled feature selection, while Versteeg, Mooij, and Zhang have focused on local constraint-based causal discovery under selection bias.

The key to the solution mentioned in the paper involves locally identifying potential parents and children of a target variable from observational data that may include latent variables. The approach leverages causal information from m-separation and V-structures to derive theoretical consistency results, bridging the gap between global and local structure learning. The method also introduces stop rules to determine whether a variable is a direct cause or effect of the target, ensuring the correctness of the approach under standard causal Markov and faithfulness conditions with infinite samples .


How were the experiments in the paper designed?

The experiments in the paper were designed to introduce a novel local causal discovery algorithm called MMB-by-MMB, specifically tailored for models with latent variables . This algorithm aimed to identify causal structures under equivalent identification conditions with significantly lower computational expense compared to existing global algorithms . The paper also validated the correctness of the MMB-by-MMB algorithm . Additionally, the results of the proposed method were analyzed in the presence of latent variables, highlighting instances where determining causes and effects solely from observational data without additional assumptions was challenging . The study emphasized the importance of exploring the utilization of background knowledge, such as leveraging data generation mechanisms or expert knowledge, to further aid in identifying causes and effects within local structures, indicating a future research direction .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the context of local causal structure learning is WIN95PTS.Net . Regarding the availability of the code, it is not explicitly mentioned in the provided context whether the code for the evaluation is open source or not. Additional information or clarification may be needed to determine the open-source status of the code used for the evaluation.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper delves into locally identifying potential parents and children of a target variable from observational data that may include latent variables, bridging the gap between global and local structure learning . The authors utilize causal information from m-separation and V-structures to derive theoretical consistency results, demonstrating the correctness of their approach under standard causal Markov and faithfulness conditions . Additionally, the experimental results on both synthetic and real-world data validate the effectiveness and efficiency of their method . The paper also discusses the importance of reducing unnecessary conditional independence tests to mitigate violations of the causal faithfulness assumption, which is a crucial aspect addressed in the experiments . Furthermore, the incorporation of elements from the Greedy Equivalence Search (GES) algorithm to enhance robustness against faithfulness violations is highlighted as a direction for future work, indicating a comprehensive approach to addressing the scientific hypotheses .


What are the contributions of this paper?

The paper "Local Causal Structure Learning in the Presence of Latent Variables" makes several contributions:

  • It discusses network modeling methods for fMRI .
  • It explores causal discovery and inference concepts along with recent methodological advances .
  • It delves into causation, prediction, and search .
  • It presents a method for local constraint-based causal discovery under selection bias .
  • It addresses the estimation of causal effects using linear non-Gaussian causal models with hidden variables .
  • It introduces a robust causal discovery algorithm against faithfulness violation .
  • It discusses elements of causal inference .
  • It covers causal inference using graphical models with the R package pcalg .
  • It explores nonlinear causal discovery with latent confounders .
  • It presents a survey of Bayesian network structure learning .
  • It discusses partial orientation and local structural learning of causal networks for prediction .
  • It addresses the discovery of local causal networks around a target to a given depth .
  • It contributes to the field of local causal pathway discovery for single-cell RNA sequencing count data .
  • It discusses the repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders .
  • It explores causal discovery using a Bayesian local causal discovery algorithm .
  • It presents a comparison of strategies for scalable causal discovery of latent variable models from mixed data .
  • It discusses estimating feedforward and feedback effective connections from fMRI time series .

What work can be continued in depth?

Further research in the field of local causal structure learning in the presence of latent variables can be expanded in the following directions:

  • Utilizing background knowledge: Exploring how to incorporate background knowledge, such as leveraging data generation mechanisms or expert knowledge, to enhance the identification of causes and effects within local structures .
  • Combining interventional and observational data: Investigating theories and methodologies that combine interventional and observational data to improve the accuracy of causal structure learning, especially in scenarios with latent variables .

Introduction
Background
Overview of causal inference in complex systems
Challenges with latent variables and causal sufficiency
Objective
To develop a novel method for causal structure learning
Introduce MMB-by-MMB: a non-parametric approach
Highlight the importance of handling latent confounders
Method
Data Collection
MMB (Minimal Markov blanket) identification
Sampling strategies for latent variables
Data Preprocessing
Handling missing data and incomplete observations
Assumptions of Markov and faithfulness
MMB-by-MMB Algorithm
MMB computation for each variable
Identification of direct causes and effects using m-separation and V-structures
Iterative refinement and pruning
Theoretical Foundation
Markov condition explanation
Faithfulness assumption and its role
Conditions for algorithm validity
Performance Evaluation
Synthetic data experiments: comparing with existing LCS methods
Real-world data analysis: precision, recall, and F1 score comparisons
Experiments and Results
Synthetic Data Analysis
Comparison of MMB-by-MMB with LCS competitors
Effectiveness and efficiency demonstration
Real-World Applications
Case studies showcasing MMB-by-MMB's performance
Challenges and practical implications
Related Work
Overview of previous approaches in causal structure learning
Discussion of methods that handle latent variables
Conclusion
Summary of MMB-by-MMB's contributions
Advantages in latent confounder detection
Future research directions and potential improvements
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
Under what conditions is the MMB-by-MMB algorithm theoretically sound?
How does MMB-by-MMB perform compared to existing LCS methods in practical applications?
What is the primary focus of the paper?
What is the proposed algorithm called in the paper?

Local Causal Structure Learning in the Presence of Latent Variables

Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng·May 25, 2024

Summary

The paper investigates local causal structure learning in scenarios with latent variables, proposing the MMB-by-MMB algorithm. This method identifies direct causes and effects of a target variable without causal sufficiency assumptions, relying on m-separation and V-structures. The algorithm is theoretically sound under Markov and faithfulness conditions and is shown to be effective and efficient through experiments on synthetic and real-world data. It compares favorably to existing LCS methods, especially in practical applications. The study also discusses related work, algorithmic details, and performance comparisons, demonstrating MMB-by-MMB's advantages in handling latent confounders and outperforming competitors in terms of precision, recall, and F1 score.
Mind map
Real-world data analysis: precision, recall, and F1 score comparisons
Synthetic data experiments: comparing with existing LCS methods
Iterative refinement and pruning
Identification of direct causes and effects using m-separation and V-structures
MMB computation for each variable
Challenges and practical implications
Case studies showcasing MMB-by-MMB's performance
Effectiveness and efficiency demonstration
Comparison of MMB-by-MMB with LCS competitors
Performance Evaluation
MMB-by-MMB Algorithm
Sampling strategies for latent variables
MMB (Minimal Markov blanket) identification
Highlight the importance of handling latent confounders
Introduce MMB-by-MMB: a non-parametric approach
To develop a novel method for causal structure learning
Challenges with latent variables and causal sufficiency
Overview of causal inference in complex systems
Future research directions and potential improvements
Advantages in latent confounder detection
Summary of MMB-by-MMB's contributions
Discussion of methods that handle latent variables
Overview of previous approaches in causal structure learning
Real-World Applications
Synthetic Data Analysis
Theoretical Foundation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Related Work
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Overview of causal inference in complex systems
Challenges with latent variables and causal sufficiency
Objective
To develop a novel method for causal structure learning
Introduce MMB-by-MMB: a non-parametric approach
Highlight the importance of handling latent confounders
Method
Data Collection
MMB (Minimal Markov blanket) identification
Sampling strategies for latent variables
Data Preprocessing
Handling missing data and incomplete observations
Assumptions of Markov and faithfulness
MMB-by-MMB Algorithm
MMB computation for each variable
Identification of direct causes and effects using m-separation and V-structures
Iterative refinement and pruning
Theoretical Foundation
Markov condition explanation
Faithfulness assumption and its role
Conditions for algorithm validity
Performance Evaluation
Synthetic data experiments: comparing with existing LCS methods
Real-world data analysis: precision, recall, and F1 score comparisons
Experiments and Results
Synthetic Data Analysis
Comparison of MMB-by-MMB with LCS competitors
Effectiveness and efficiency demonstration
Real-World Applications
Case studies showcasing MMB-by-MMB's performance
Challenges and practical implications
Related Work
Overview of previous approaches in causal structure learning
Discussion of methods that handle latent variables
Conclusion
Summary of MMB-by-MMB's contributions
Advantages in latent confounder detection
Future research directions and potential improvements

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of discovering causal relationships from observational data, specifically in the presence of latent variables . This problem is not entirely new, as there have been existing methods and algorithms developed to recover causal structures among observed variables in the presence of latent variables . The paper focuses on locally identifying potential parents and children of a target variable from observational data that may include latent variables, bridging the gap between global and local structure learning . The research delves into deriving theoretical consistency results and presenting a principled method for determining direct cause or effect relationships under certain conditions, contributing to the field of causal inference and local causal structure learning .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to local causal structure learning in the presence of latent variables . The primary focus is on identifying potential parents and children of a target variable from observational data that may include latent variables . The paper delves into deriving theoretical consistency results by utilizing causal information from m-separation and V-structures, bridging the gap between global and local structure learning . The goal is to determine whether a variable is a direct cause or effect of a target by developing a principled method based on causal Markov and faithfulness conditions . The approach presented in the paper is theoretically validated under standard causal conditions with infinite samples, and its effectiveness and efficiency are confirmed through experimental results on synthetic and real-world data .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper introduces a novel local causal discovery algorithm called MMB-by-MMB, specifically designed to effectively identify causal structures in models with latent variables . This algorithm focuses on learning local causal relationships rather than the global causal structure, which is often more relevant in real-world scenarios . Unlike existing global algorithms, the MMB-by-MMB method demonstrates the capability to identify causal structures under equivalent identification conditions with significantly lower computational expense . Additionally, the paper provides proof validating the correctness of the MMB-by-MMB algorithm .

Furthermore, the study acknowledges the challenges posed by latent variables in determining causes and effects solely from observational data without additional assumptions. To address this, the paper suggests exploring the utilization of background knowledge, such as leveraging data generation mechanisms or expert knowledge, to further aid in identifying causes and effects within local structures, which remains a future research direction . This approach aligns with the need to enhance causal discovery methods by incorporating external information to improve the accuracy of causal relationships . The MMB-by-MMB algorithm proposed in the paper offers several key characteristics and advantages compared to previous methods in local causal structure learning .

  1. Efficiency and Computational Cost: The MMB-by-MMB algorithm is designed to identify causal structures in models with latent variables with significantly lower computational expense compared to existing global algorithms . This efficiency is crucial for practical applications where computational resources are limited.

  2. Local Causal Discovery: Unlike global algorithms that focus on learning the entire causal graph, the MMB-by-MMB method concentrates on local causal relationships, specifically targeting the direct causes and effects of a target variable based on the estimated local structure . This local approach is beneficial in scenarios where understanding the causal relationships surrounding a single target variable is more relevant.

  3. Validation and Correctness: The paper provides proof validating the correctness of the MMB-by-MMB algorithm, ensuring its reliability and accuracy in identifying causal structures in the presence of latent variables .

  4. Future Research Directions: Acknowledging the challenges posed by latent variables in causal discovery, the paper suggests exploring the utilization of background knowledge, such as leveraging data generation mechanisms or expert knowledge, to further aid in identifying causes and effects within local structures . This highlights a future research direction to enhance causal discovery methods by incorporating external information for improved accuracy.

  5. Experimental Efficacy: Through extensive experiments, the paper demonstrates the efficacy of the MMB-by-MMB algorithm on both benchmark network structures and real-world data, showcasing its practical utility and performance .

In summary, the MMB-by-MMB algorithm stands out for its efficiency, focus on local causal relationships, validation of correctness, potential for leveraging background knowledge, and demonstrated efficacy in experimental settings, offering a promising approach for causal structure learning in the presence of latent variables.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of local causal structure learning in the presence of latent variables. Noteworthy researchers in this area include Spirtes, Glymour, and Scheines , who have contributed to the understanding of causation, prediction, and search. Additionally, Tsamardinos and Aliferis have worked towards principled feature selection, while Versteeg, Mooij, and Zhang have focused on local constraint-based causal discovery under selection bias.

The key to the solution mentioned in the paper involves locally identifying potential parents and children of a target variable from observational data that may include latent variables. The approach leverages causal information from m-separation and V-structures to derive theoretical consistency results, bridging the gap between global and local structure learning. The method also introduces stop rules to determine whether a variable is a direct cause or effect of the target, ensuring the correctness of the approach under standard causal Markov and faithfulness conditions with infinite samples .


How were the experiments in the paper designed?

The experiments in the paper were designed to introduce a novel local causal discovery algorithm called MMB-by-MMB, specifically tailored for models with latent variables . This algorithm aimed to identify causal structures under equivalent identification conditions with significantly lower computational expense compared to existing global algorithms . The paper also validated the correctness of the MMB-by-MMB algorithm . Additionally, the results of the proposed method were analyzed in the presence of latent variables, highlighting instances where determining causes and effects solely from observational data without additional assumptions was challenging . The study emphasized the importance of exploring the utilization of background knowledge, such as leveraging data generation mechanisms or expert knowledge, to further aid in identifying causes and effects within local structures, indicating a future research direction .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the context of local causal structure learning is WIN95PTS.Net . Regarding the availability of the code, it is not explicitly mentioned in the provided context whether the code for the evaluation is open source or not. Additional information or clarification may be needed to determine the open-source status of the code used for the evaluation.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper delves into locally identifying potential parents and children of a target variable from observational data that may include latent variables, bridging the gap between global and local structure learning . The authors utilize causal information from m-separation and V-structures to derive theoretical consistency results, demonstrating the correctness of their approach under standard causal Markov and faithfulness conditions . Additionally, the experimental results on both synthetic and real-world data validate the effectiveness and efficiency of their method . The paper also discusses the importance of reducing unnecessary conditional independence tests to mitigate violations of the causal faithfulness assumption, which is a crucial aspect addressed in the experiments . Furthermore, the incorporation of elements from the Greedy Equivalence Search (GES) algorithm to enhance robustness against faithfulness violations is highlighted as a direction for future work, indicating a comprehensive approach to addressing the scientific hypotheses .


What are the contributions of this paper?

The paper "Local Causal Structure Learning in the Presence of Latent Variables" makes several contributions:

  • It discusses network modeling methods for fMRI .
  • It explores causal discovery and inference concepts along with recent methodological advances .
  • It delves into causation, prediction, and search .
  • It presents a method for local constraint-based causal discovery under selection bias .
  • It addresses the estimation of causal effects using linear non-Gaussian causal models with hidden variables .
  • It introduces a robust causal discovery algorithm against faithfulness violation .
  • It discusses elements of causal inference .
  • It covers causal inference using graphical models with the R package pcalg .
  • It explores nonlinear causal discovery with latent confounders .
  • It presents a survey of Bayesian network structure learning .
  • It discusses partial orientation and local structural learning of causal networks for prediction .
  • It addresses the discovery of local causal networks around a target to a given depth .
  • It contributes to the field of local causal pathway discovery for single-cell RNA sequencing count data .
  • It discusses the repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders .
  • It explores causal discovery using a Bayesian local causal discovery algorithm .
  • It presents a comparison of strategies for scalable causal discovery of latent variable models from mixed data .
  • It discusses estimating feedforward and feedback effective connections from fMRI time series .

What work can be continued in depth?

Further research in the field of local causal structure learning in the presence of latent variables can be expanded in the following directions:

  • Utilizing background knowledge: Exploring how to incorporate background knowledge, such as leveraging data generation mechanisms or expert knowledge, to enhance the identification of causes and effects within local structures .
  • Combining interventional and observational data: Investigating theories and methodologies that combine interventional and observational data to improve the accuracy of causal structure learning, especially in scenarios with latent variables .
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.