Relaxation of Some Confusions about Confounders

Area of Science:

• Causal inference
• Dynamic systems analysis
• Machine learning

Background:

• Causal discovery aims to infer cause-effect relationships from observational data.
• Dynamic systems present unique challenges due to temporal dependencies.
• Standard causal discovery methods often assume specific data distributions or system properties.

Purpose of the Study:

• To investigate observational causal discovery for both deterministic and stochastic dynamic systems.
• To evaluate the added value of standard conditional independence tests in uncovering system dynamics.
• To explore the implications of systems residing in geodesic spaces for causal discovery.

Main Methods:

• Utilizing standard conditional independence tests within causal discovery frameworks.
• Applying methods to both deterministic and stochastic dynamic system models.
• Analyzing system interactions within the context of geodesic spaces.

Main Results:

• Conditional independence tests provide valuable insights into system interactions beyond standard assumptions.
• The effectiveness of these tests is influenced by the system's dynamic properties (deterministic vs. stochastic).
• Geodesic space properties can impact the identifiability and accuracy of causal discovery.

Conclusions:

• Observational causal discovery in dynamic systems can be enhanced by conditional independence tests.
• The choice of methods should consider system determinism/stochasticity and underlying space geometry.
• Further research is needed to fully leverage geometric properties for robust causal inference.