cispa_all_3729.pdf (634.22 kB)

Inferring Cause and Effect in the Presence of Heteroscedastic Noise

Download (634.22 kB)
conference contribution
posted on 2023-11-29, 18:21 authored by Sascha XuSascha Xu, Osman Mian, Jilles VreekenJilles Vreeken
We study the problem of identifying cause and effect over two univariate continuous variables X and Y from a sample of their joint distribu- tion. Our focus lies on the setting where the variance of the noise may be dependent on the cause. We propose to partition the domain of the cause into multiple segments when the noise in- deed is dependent. To this end, we minimize a scale-invariant, penalized regression score, find- ing the optimal partitioning using dynamic pro- gramming. We show under which conditions this allows us to identify the causal direction for the linear setting with heteroscedastic noise, for the non-linear setting with homoscedastic noise, as well as empirically confirm that these results generalize to the non-linear and heteroscedas- tic case. Altogether, the ability to model het- eroscedasticity translates into an improved per- formance in telling cause from effect on a wide range of synthetic and real-world datasets.


Preferred Citation

Sascha Xu, Osman Mian and Jilles Vreeken. Inferring Cause and Effect in the Presence of Heteroscedastic Noise. In: International Conference on Machine Learning (ICML). 2022.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

International Conference on Machine Learning (ICML)

Legacy Posted Date


Open Access Type

  • Unknown


@inproceedings{cispa_all_3729, title = "Inferring Cause and Effect in the Presence of Heteroscedastic Noise", author = "Xu, Sascha and Mian, Osman and Vreeken, Jilles", booktitle="{International Conference on Machine Learning (ICML)}", year="2022", }

Usage metrics


    No categories selected


    Ref. manager