Gradient-Based Local Causal Structure Learning.

Area of Science:

• Causal inference
• Machine learning
• Statistical modeling

Background:

• Discovering causal structure from observational data is vital across scientific disciplines.
• Existing methods for local causal structure (LCS) learning struggle with noisy data and often fail to orient all causal edges.
• Conditional independence (CI) tests, commonly used in LCS algorithms, are unreliable with real-world data complexities.

Purpose of the Study:

• To develop a more accurate and robust approach for learning local causal structure (LCS).
• To simultaneously determine causal neighbors and orient edges, overcoming limitations of existing methods.
• To provide a gradient-based framework for efficient optimization of causal graph search.

Main Methods:

• Proposes GraN-LCS, a gradient-based method for simultaneous neighbor determination and edge orientation.
• Formulates causal graph search as minimizing an acyclicity-regularized score function, optimized via gradient descent.
• Utilizes a multilayer perceptron (MLP) for fitting variables and an acyclicity-constrained loss for local graph recovery, incorporating preliminary neighborhood selection (PNS) and L1-norm regularization.

Main Results:

• GraN-LCS demonstrates superior accuracy in learning local causal structure compared to state-of-the-art baselines.
• Experiments on synthetic and real-world datasets validate the efficacy of the proposed method.
• Ablation studies confirm the significant contribution of key components within the GraN-LCS framework.

Conclusions:

• GraN-LCS offers an effective gradient-based solution for accurate local causal structure learning.
• The method addresses limitations of CI-test-based approaches, particularly in challenging real-world scenarios.
• GraN-LCS provides a robust framework for identifying direct causes and effects, enhancing causal discovery.