MIIC

MIIC infers causal relationships and network structure from multivariate observational data using information-theoretic measures and Bayesian-network modeling while accounting for latent variables.

Key Features:

• Inclusion of Latent Variables: Incorporates the effects of unobserved latent variables to model dependencies when data are incomplete or missing.
• Handling of Non-Causal Relationships: Distinguishes causal from non-causal statistical dependencies to reduce spurious causal claims.
• Scalability: Scales to high-dimensional, large datasets for efficient inference.
• Interpretability: Produces inferred network structures that expose relationships between variables for interpretation and validation.

Scientific Applications:

• Genomics: Identifies causal relationships between genetic markers and phenotypic traits in studies of complex diseases.
• Environmental Science: Analyzes the impact of environmental factors on ecological systems.
• Economics: Uncovers causal links between economic policies and market outcomes.

Methodology:

Grounded in information theory, leveraging entropy and mutual information measures to infer causal relationships, with data preprocessing and feature selection followed by application of Bayesian networks to model dependencies between variables.