Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

Understand

Causal inference using observational data is challenging, especially in the bivariate case.

• Through the minimum description length principle, we link the postulate of independence between the generating mechanisms of the cause and of the effect given the cause to quantile regression.
• Based on this theory, we develop Bivariate Quantile Causal Discovery (bQCD), a new method to distinguish cause from effect assuming no confounding, selection bias or feedback.
• Because it uses multiple quantile levels instead of the conditional mean only, bQCD is adaptive not only to additive, but also to multiplicative or even location-scale generating mechanisms.