Causal Learning via Manifold Regularization.

Area of Science:

• Computational Biology
• Machine Learning
• Causal Inference

Background:

• Estimating causal structures from observational data is a significant challenge in various scientific fields.
• Existing methods often require extensive prior knowledge or controlled interventions.

Purpose of the Study:

• To propose a novel machine learning framework for causal structure estimation.
• To develop a method that integrates observational data with limited background knowledge or interventional data.

Main Methods:

• Causal structure estimation is framed as a machine learning task, where causal relationships are treated as labels.
• A distance-based approach utilizing bivariate histograms is developed within a manifold regularization framework.
• The method handles partially labeled data, combining background scientific knowledge or interventional data with unlabeled observational data.

Main Results:

• Empirical validation was performed on three distinct biological datasets.
• The approach demonstrated efficacy in accurately estimating causal structures, even with limited labeled information.
• Results showed the method's general applicability and user-friendliness, with some causal effects verifiable through experimental intervention.

Conclusions:

• The proposed machine learning framework offers a powerful and versatile approach to causal structure estimation.
• This method simplifies the process of inferring causal relationships in complex biological systems.
• The findings highlight the potential of integrating machine learning with domain knowledge for advancing causal discovery.