Causal Discovery with Flow-based Conditional Density Estimation

Shaogang Ren, Haiyan Yin, Mingming Sun, Ping Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Causal-effect discovery plays an essential role in many disciplines of science and real-world applications. In this paper, we introduce a new causal discovery method to solve the classic problem of inferring the causal direction under a bivariate setting. In particular, our proposed method first leverages a flow model to estimate the joint probability density of the variables. Then we formulate a novel evaluation metric to infer the scores for each potential causal direction based on the variance of the conditional density estimation. By leveraging the flow-based conditional density estimation metric, our causal discovery approach alleviates the restrictive assumptions made by the conventional methods, such as assuming the linearity relationship between the two variables. Therefore, it could potentially be able to better capture the complex causal relationship among data in various problem domains that comes in arbitrary forms. We conduct extensive evaluations to compare our method with decent causal discovery approaches. Empirical results show that our method could promisingly outperform the baseline methods with noticeable margins on both synthetic and real-world datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 21st IEEE International Conference on Data Mining, ICDM 2021
EditorsJames Bailey, Pauli Miettinen, Yun Sing Koh, Dacheng Tao, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665423984
StatePublished - 2021
Event21st IEEE International Conference on Data Mining, ICDM 2021 - Virtual, Online, New Zealand
Duration: Dec 7 2021Dec 10 2021

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference21st IEEE International Conference on Data Mining, ICDM 2021
Country/TerritoryNew Zealand
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • General Engineering


  • causal inference
  • density estimation
  • neural networks
  • normalizing flow

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