On the identifiability and estimation of causal location-scale noise models
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the
effect $ Y $ can be written as a function of the cause $ X $ and a noise source $ N …
effect $ Y $ can be written as a function of the cause $ X $ and a noise source $ N …
Inferring cause and effect in the presence of heteroscedastic noise
We study the problem of identifying cause and effect over two univariate continuous
variables $ X $ and $ Y $ from a sample of their joint distribution. Our focus lies on the …
variables $ X $ and $ Y $ from a sample of their joint distribution. Our focus lies on the …
Learning causal models under independent changes
S Mameche, D Kaltenpoth… - Advances in Neural …, 2024 - proceedings.neurips.cc
In many scientific applications, we observe a system in different conditions in which its
components may change, rather than in isolation. In our work, we are interested in …
components may change, rather than in isolation. In our work, we are interested in …
Adversarial balancing-based representation learning for causal effect inference with observational data
X Du, L Sun, W Duivesteijn, A Nikolaev… - Data Mining and …, 2021 - Springer
Learning causal effects from observational data greatly benefits a variety of domains such as
health care, education, and sociology. For instance, one could estimate the impact of a new …
health care, education, and sociology. For instance, one could estimate the impact of a new …
Discovering fully oriented causal networks
We study the problem of inferring causal graphs from observational data. We are particularly
interested in discovering graphs where all edges are oriented, as opposed to the partially …
interested in discovering graphs where all edges are oriented, as opposed to the partially …
Information-theoretic causal discovery and intervention detection over multiple environments
Given multiple datasets over a fixed set of random variables, each collected from a different
environment, we are interested in discovering the shared underlying causal network and the …
environment, we are interested in discovering the shared underlying causal network and the …
Three-stage root cause analysis for logistics time efficiency via explainable machine learning
S Hao, Y Liu, Y Wang, Y Wang, W Zhe - Proceedings of the 28th ACM …, 2022 - dl.acm.org
The performance of logistics highly depends on the time efficiency, and hence, plenty of
efforts have been devoted to ensuring the on-time delivery in modern logistics industry …
efforts have been devoted to ensuring the on-time delivery in modern logistics industry …
Towards efficient local causal structure learning
Local causal structure learning aims to discover and distinguish direct causes (parents) and
direct effects (children) of a variable of interest from data. While emerging successes have …
direct effects (children) of a variable of interest from data. While emerging successes have …
Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice
D Machlanski, S Samothrakis… - Causal Learning and …, 2024 - proceedings.mlr.press
Hyperparameters play a critical role in machine learning. Hyperparameter tuning can make
the difference between state-of-the-art and poor prediction performance for any algorithm …
the difference between state-of-the-art and poor prediction performance for any algorithm …
Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery
N Tagasovska, V Chavez-Demoulin… - … on Machine Learning, 2020 - proceedings.mlr.press
Causal inference using observational data is challenging, especially in the bivariate case.
Through the minimum description length principle, we link the postulate of independence …
Through the minimum description length principle, we link the postulate of independence …