The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
- URL: http://arxiv.org/abs/2404.11341v1
- Date: Wed, 17 Apr 2024 13:00:52 GMT
- Title: The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
- Authors: Juan L. Gamella, Jonas Peters, Peter Bühlmann,
- Abstract summary: In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets.
We have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems.
- Score: 10.81691411087626
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited information about the applicability of the proposed methods to real problems. As a step forward, we have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems. The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a variety of fields. We illustrate potential applications through a series of case studies in fields such as causal discovery, out-of-distribution generalization, change point detection, independent component analysis, and symbolic regression. For applications to causal inference, the chambers allow us to carefully perform interventions. We also provide and empirically validate a causal model of each chamber, which can be used as ground truth for different tasks. All hardware and software is made open source, and the datasets are publicly available at causalchamber.org or through the Python package causalchamber.
Related papers
- Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing [1.8877926393541125]
We show that we can abduce movement trajectories efficiently through an informed (i.e., A*) search.
We also report on our own experiments showing that we not only provide exact results but also scale to very large scenarios.
arXiv Detail & Related papers (2024-07-08T23:11:47Z) - Interactive System-wise Anomaly Detection [66.3766756452743]
Anomaly detection plays a fundamental role in various applications.
It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data.
We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
arXiv Detail & Related papers (2023-04-21T02:20:24Z) - Learning to Bound Counterfactual Inference in Structural Causal Models
from Observational and Randomised Data [64.96984404868411]
We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm.
The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources.
It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
arXiv Detail & Related papers (2022-12-06T12:42:11Z) - Lightweight Automated Feature Monitoring for Data Streams [1.4658400971135652]
We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets.
It monitors all features that are used by the system, while providing an interpretable features ranking whenever an alarm occurs.
This illustrates how FM eliminates the need to add custom signals to detect specific types of problems and that monitoring the available space of features is often enough.
arXiv Detail & Related papers (2022-07-18T14:38:11Z) - Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data.
We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism.
We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
arXiv Detail & Related papers (2022-06-30T06:00:13Z) - Amortized Inference for Causal Structure Learning [72.84105256353801]
Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test.
We train a variational inference model to predict the causal structure from observational/interventional data.
Our models exhibit robust generalization capabilities under substantial distribution shift.
arXiv Detail & Related papers (2022-05-25T17:37:08Z) - An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols.
We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
arXiv Detail & Related papers (2021-08-09T17:39:09Z) - Occupancy Detection in Room Using Sensor Data [0.0]
This paper provides a solution to detect occupancy using sensor data by using and testing several variables.
Seven famous algorithms in Machine Learning, namely as Decision Tree, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, Kernelized SVM and K-Nearest Neighbors are tested and compared.
arXiv Detail & Related papers (2021-01-10T19:53:57Z) - Real-World Anomaly Detection by using Digital Twin Systems and
Weakly-Supervised Learning [3.0100975935933567]
We present novel weakly-supervised approaches to anomaly detection for industrial settings.
The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery.
The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset.
arXiv Detail & Related papers (2020-11-12T10:15:56Z) - Meta Learning for Causal Direction [29.00522306460408]
We introduce a novel generative model that allows distinguishing cause and effect in the small data setting.
We demonstrate our method on various synthetic as well as real-world data and show that it is able to maintain high accuracy in detecting directions across varying dataset sizes.
arXiv Detail & Related papers (2020-07-06T15:12:05Z) - AutoOD: Automated Outlier Detection via Curiosity-guided Search and
Self-imitation Learning [72.99415402575886]
Outlier detection is an important data mining task with numerous practical applications.
We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model.
Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
arXiv Detail & Related papers (2020-06-19T18:57:51Z)
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.