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Summary
The paper proposes a method for causal discovery on dependent binary data, which is common in real-world applications. The authors develop a pairwise maximum likelihood method to estimate the covariance matrix among units and an EM-like algorithm to generate and decorrelate samples of the latent utility variables. The method is shown to improve the accuracy of causal graph learning on both synthetic and real-world datasets.
Highlights
- The proposed method addresses the challenge of dependent binary data in causal discovery.
- A pairwise maximum likelihood method is developed to estimate the covariance matrix among units.
- An EM-like algorithm is used to generate and decorrelate samples of the latent utility variables.
- The method is evaluated on synthetic and real-world datasets, showing improved accuracy in causal graph learning.
- The authors discuss the robustness of the method to violations of model assumptions.
- The method is applied to a real-world dataset of single-cell RNA sequencing data.
- The results demonstrate the importance of considering dependence among units in causal discovery.
Key Insights
- The proposed method provides a novel approach to causal discovery on dependent binary data, which is a common challenge in many applications.
- The pairwise maximum likelihood method for estimating the covariance matrix among units is an efficient and effective approach.
- The EM-like algorithm for generating and decorrelating samples of the latent utility variables is a key component of the method, allowing for the estimation of the causal graph.
- The method's robustness to violations of model assumptions is an important feature, as real-world data often deviates from idealized assumptions.
- The application of the method to single-cell RNA sequencing data demonstrates its potential in real-world applications.
- The results highlight the importance of considering dependence among units in causal discovery, which is often neglected in traditional methods.
- The proposed method has the potential to improve the accuracy of causal graph learning in a wide range of applications, from social sciences to biology.
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Citation
Chen, A., & Zhou, Q. (2024). Causal Discovery on Dependent Binary Data (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2412.20289