Causal discovery from temporal data: An overview and new perspectives
Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …
been a typical data structure that can be widely generated by many domains, such as …
Constructing causal life-course models: Comparative study of data-driven and theory-driven approaches
AH Petersen, CT Ekstrøm, P Spirtes… - American Journal of …, 2023 - academic.oup.com
Life-course epidemiology relies on specifying complex (causal) models that describe how
variables interplay over time. Traditionally, such models have been constructed by perusing …
variables interplay over time. Traditionally, such models have been constructed by perusing …
Learned Causal Method Prediction
For a given causal question, it is important to efficiently decide which causal inference
method to use for a given dataset. This is challenging because causal methods typically rely …
method to use for a given dataset. This is challenging because causal methods typically rely …
Causal Discovery for Rolling Bearing Fault under Missing Data: From the Perspective of Causal Effect and Information Flow
X Ding, H Wu, J Wang, J Xu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Missing data phenomenon is ubiquitous in many domains which imposes difficulties on
reconstruction of causal relationships due to incomplete dataset. Currently, although causal …
reconstruction of causal relationships due to incomplete dataset. Currently, although causal …
Embracing the black box: Heading towards foundation models for causal discovery from time series data
Causal discovery from time series data encompasses many existing solutions, including
those based on deep learning techniques. However, these methods typically do not endorse …
those based on deep learning techniques. However, these methods typically do not endorse …
Log-Paradox: Necessary and sufficient conditions for confounding statistically significant pattern reversal under the log-transform
The log-transform is a common tool in statistical analysis, reducing the impact of extreme
values, compressing the range of reported values for improved visualization, enabling the …
values, compressing the range of reported values for improved visualization, enabling the …
Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms
AH Petersen - arXiv preprint arXiv:2412.10039, 2024 - arxiv.org
New proposals for causal discovery algorithms are typically evaluated using simulations and
a few select real data examples with known data generating mechanisms. However, there …
a few select real data examples with known data generating mechanisms. However, there …
Learning domain-specific causal discovery from time series
X Wang, KP Kording - arXiv preprint arXiv:2209.05598, 2022 - arxiv.org
Causal discovery (CD) from time-varying data is important in neuroscience, medicine, and
machine learning. Techniques for CD encompass randomized experiments, which are …
machine learning. Techniques for CD encompass randomized experiments, which are …
CD-NOTEARS: Concept Driven Causal Structure Learning Using NOTEARS
J Chowdhury, G Terejanu - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Causal discovery has become increasingly popular in recent years, with the emergence of
various methods for inferring causal relationships from observational data. While NOTEARS …
various methods for inferring causal relationships from observational data. While NOTEARS …
Leveraging Domain Knowledge for Enhanced Causal Structure Learning and Out-of-Distribution Generalization in Observational Data
MHJ Chowdhury - 2024 - search.proquest.com
Causal modeling enables robust counterfactual reasoning and interventional mechanisms
to make predictions across different hypothetical scenarios. Nevertheless, uncovering …
to make predictions across different hypothetical scenarios. Nevertheless, uncovering …