Causal Discovery from Discrete Data using Hidden Compact Representation.

Area of Science:

• Causal inference
• Machine learning
• Statistical modeling

Background:

• Causal discovery is crucial across disciplines but challenging for categorical data.
• Existing methods for continuous variables struggle with compact causal mechanism descriptions for categorical data.

Purpose of the Study:

• To develop an effective causal discovery method for categorical data.
• To address the challenge of finding compact causal mechanisms in categorical causal discovery.

Main Methods:

• Proposing a two-stage causal process: cause to hidden variable (lower cardinality), then hidden variable to effect.
• Utilizing a likelihood framework to recover the hidden compact representation.
• Demonstrating identifiability of causal direction under weak conditions.

Main Results:

• The proposed two-stage model allows for a simple and compact representation of causal mechanisms.
• Causal direction is identifiable under specific, weak conditions.
• Empirical studies confirm the method's effectiveness on both synthetic and real-world data.

Conclusions:

• The novel two-stage approach provides an effective solution for causal discovery with categorical data.
• The method successfully identifies causal direction and recovers underlying mechanisms.
• This work advances causal inference techniques for discrete variables.