Deep Learning of Causal Structures in High Dimensions
- URL: http://arxiv.org/abs/2212.04866v1
- Date: Fri, 9 Dec 2022 14:12:47 GMT
- Title: Deep Learning of Causal Structures in High Dimensions
- Authors: Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee
- Abstract summary: We propose a deep neural architecture for learning causal relationships between variables from a combination of empirical data and prior causal knowledge.
We combine convolutional and graph neural networks within a causal risk framework to provide a flexible and scalable approach.
- Score: 0.6021787236982659
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Recent years have seen rapid progress at the intersection between causality
and machine learning. Motivated by scientific applications involving
high-dimensional data, in particular in biomedicine, we propose a deep neural
architecture for learning causal relationships between variables from a
combination of empirical data and prior causal knowledge. We combine
convolutional and graph neural networks within a causal risk framework to
provide a flexible and scalable approach. Empirical results include linear and
nonlinear simulations (where the underlying causal structures are known and can
be directly compared against), as well as a real biological example where the
models are applied to high-dimensional molecular data and their output compared
against entirely unseen validation experiments. These results demonstrate the
feasibility of using deep learning approaches to learn causal networks in
large-scale problems spanning thousands of variables.
Related papers
- Learning to refine domain knowledge for biological network inference [2.209921757303168]
Perturbation experiments allow biologists to discover causal relationships between variables of interest.
The sparsity and high dimensionality of these data pose significant challenges for causal structure learning algorithms.
We propose an amortized algorithm for refining domain knowledge, based on data observations.
arXiv Detail & Related papers (2024-10-18T12:53:23Z) - Online Multi-modal Root Cause Analysis [61.94987309148539]
Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems.
Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems.
We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
arXiv Detail & Related papers (2024-10-13T21:47:36Z) - Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
We propose a novel machine learning approach for inferring causal variables of a target variable from observations.
We employ a neural network trained to identify causality through supervised learning on simulated data.
Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks.
arXiv Detail & Related papers (2024-08-29T02:21:11Z) - Causal machine learning for single-cell genomics [94.28105176231739]
We discuss the application of machine learning techniques to single-cell genomics and their challenges.
We first present the model that underlies most of current causal approaches to single-cell biology.
We then identify open problems in the application of causal approaches to single-cell data.
arXiv Detail & Related papers (2023-10-23T13:35:24Z) - Predicting Biomedical Interactions with Probabilistic Model Selection
for Graph Neural Networks [5.156812030122437]
Current biological networks are noisy, sparse, and incomplete. Experimental identification of such interactions is both time-consuming and expensive.
Deep graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction.
Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.
arXiv Detail & Related papers (2022-11-22T20:44:28Z) - Physically constrained neural networks to solve the inverse problem for
neuron models [0.29005223064604074]
Systems biology and systems neurophysiology are powerful tools for a number of key applications in the biomedical sciences.
Recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal approximators.
arXiv Detail & Related papers (2022-09-24T12:51:15Z) - Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs.
By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
arXiv Detail & Related papers (2022-02-01T17:11:13Z) - Learning Neural Causal Models with Active Interventions [83.44636110899742]
We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process.
Our method significantly reduces the required number of interactions compared with random intervention targeting.
We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
arXiv Detail & Related papers (2021-09-06T13:10:37Z) - Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
We propose a novel learning framework for inference and estimation problems of diffusion on networks.
Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities.
Our approach is versatile and robust to variations of the underlying diffusion network models.
arXiv Detail & Related papers (2020-06-16T18:45:20Z) - Causal Discovery from Incomplete Data: A Deep Learning Approach [21.289342482087267]
Imputated Causal Learning is proposed to perform iterative missing data imputation and causal structure discovery.
We show that ICL can outperform state-of-the-art methods under different missing data mechanisms.
arXiv Detail & Related papers (2020-01-15T14:28:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.