Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Casual Discovery

Jul 12, 2020

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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 Quantile Causal Discovery (QCD), a new method to uncover causal relationships. Because it uses multiple quantile levels instead of the conditional mean only, QCD is adaptive not only to additive, but also to multiplicative or even location-scale generating mechanisms. To illustrate the empirical effectiveness of our approach, we perform an extensive empirical comparison on both synthetic and real datasets. This study shows that QCD is robust across different implementations of the method (i.e., the quantile regression algorithm), computationally efficient, and compares favorably to state-of-the-art methods.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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