https://techxplore.com/news/2024-10-revealing-causal-links-complex-algorithm.html logotype Topics * Week's top * Latest news * Unread news * Subscribe [ ] Science X Account [ ] [ ] [*] Remember me Sign In Click here to sign in with or Forget Password? Not a member? Sign up Learn more * Automotive * Business * Computer Sciences * Consumer & Gadgets * Electronics & Semiconductors * Energy & Green Tech * Engineering * Hardware * Hi Tech & Innovation * Internet * Machine learning & AI * Other * Robotics * Security * Software * Telecom [INS::INS] * * share this! * 62 * Twit * Share * Email 1. Home 2. Engineering 1. Home 2. Computer Sciences * * * --------------------------------------------------------------------- November 1, 2024 Editors' notes This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked peer-reviewed publication trusted source proofread Revealing causal links in complex systems: New algorithm reveals hidden influences by Massachusetts Institute of Technology cause effect Credit: CC0 Public Domain Getting to the heart of causality is central to understanding the world around us. What causes one variable--be it a biological species, a voting region, a company stock, or a local climate--to shift from one state to another can inform how we might shape that variable in the future. But tracing an effect to its root cause can quickly become intractable in real-world systems, where many variables can converge, confound, and cloud over any causal links. Now, a team of MIT engineers hopes to provide some clarity in the pursuit of causality. They developed a method that can be applied to a wide range of situations to identify those variables that likely influence other variables in a complex system. The method, in the form of an algorithm, takes in data that have been collected over time, such as the changing populations of different species in a marine environment. From those data, the method measures the interactions between every variable in a system and estimates the degree to which a change in one variable (say, the number of sardines in a region over time) can predict the state of another (such as the population of anchovy in the same region). The engineers then generate a "causality map" that links variables that likely have some sort of cause-and-effect relationship. The algorithm determines the specific nature of that relationship, such as whether two variables are synergistic--meaning one variable only influences another if it is paired with a second variable--or redundant, such that a change in one variable can have exactly the same, and therefore redundant, effect as another variable. The new algorithm can also make an estimate of "causal leakage," or the degree to which a system's behavior cannot be explained through the variables that are available; some unknown influence must be at play, and therefore, more variables must be considered. "The significance of our method lies in its versatility across disciplines," says Alvaro Martinez-Sanchez, a graduate student in MIT's Department of Aeronautics and Astronautics (AeroAstro). "It can be applied to better understand the evolution of species in an ecosystem, the communication of neurons in the brain, and the interplay of climatological variables between regions, to name a few examples." For their part, the engineers plan to use the algorithm to help solve problems in aerospace, such as identifying features in aircraft design that can reduce a plane's fuel consumption. "We hope by embedding causality into models, it will help us better understand the relationship between design variables of an aircraft and how it relates to efficiency," says Adrian Lozano-Duran, an associate professor at AeroAstro. The engineers, along with MIT postdoc Gonzalo Arranz, have published their results in Nature Communications. [INS::INS] Seeing connections In recent years, a number of computational methods have been developed to take in data about complex systems and identify causal links between variables in the system, based on certain mathematical descriptions that should represent causality. "Different methods use different mathematical definitions to determine causality," Lozano-Duran notes. "There are many possible definitions that all sound OK, but they may fail under some conditions." In particular, he says that existing methods are not designed to tell the difference between certain types of causality. Namely, they don't distinguish between a "unique" causality, in which one variable has a unique effect on another, apart from every other variable, from a "synergistic" or a "redundant" link. An example of a synergistic causality would be if one variable (say, the action of drug A) had no effect on another variable (a person's blood pressure), unless the first variable was paired with a second (drug B). An example of redundant causality would be if one variable (a student's work habits) affects another variable (their chance of getting good grades), but that effect has the same impact as another variable (the amount of sleep the student gets). "Other methods rely on the intensity of the variables to measure causality," adds Arranz. "Therefore, they may miss links between variables whose intensity is not strong yet they are important." Messaging rates In their new approach, the engineers took a page from information theory--the science of how messages are communicated through a network, based on a theory formulated by the late MIT professor emeritus Claude Shannon. The team developed an algorithm to evaluate any complex system of variables as a messaging network. "We treat the system as a network, and variables transfer information to each other in a way that can be measured," Lozano-Duran explains. "If one variable is sending messages to another, that implies it must have some influence. That's the idea of using information propagation to measure causality." The new algorithm evaluates multiple variables simultaneously, rather than taking on one pair of variables at a time, as other methods do. The algorithm defines information as the likelihood that a change in one variable will also see a change in another. This likelihood--and therefore, the information that is exchanged between variables--can get stronger or weaker as the algorithm evaluates more data of the system over time. In the end, the method generates a map of causality that shows which variables in the network are strongly linked. From the rate and pattern of these links, the researchers can then distinguish which variables have a unique, synergistic, or redundant relationship. By this same approach, the algorithm can also estimate the amount of "causality leak" in the system, meaning the degree to which a system's behavior cannot be predicted based on the information available. [INS::INS] "Part of our method detects if there's something missing," Lozano-Duran says. "We don't know what is missing, but we know we need to include more variables to explain what is happening." The team applied the algorithm to a number of benchmark cases that are typically used to test causal inference. These cases range from observations of predator-prey interactions over time, to measurements of air temperature and pressure in different geographic regions, and the co-evolution of multiple species in a marine environment. The algorithm successfully identified causal links in every case, compared with most methods that can only handle some cases. The method, which the team coined SURD, for Synergistic-Unique-Redundant Decomposition of causality, is available online for others to test on their own systems. "SURD has the potential to drive progress across multiple scientific and engineering fields, such as climate research, neuroscience, economics, epidemiology, social sciences, and fluid dynamics, among others areas," Martinez-Sanchezsays. More information: Decomposing causality into its synergistic, unique, and redundant components, Nature Communications (2024). Journal information: Nature Communications Provided by Massachusetts Institute of Technology This story is republished courtesy of MIT News (web.mit.edu/ newsoffice/), a popular site that covers news about MIT research, innovation and teaching. Citation: Revealing causal links in complex systems: New algorithm reveals hidden influences (2024, November 1) retrieved 12 November 2024 from https://techxplore.com/news/ 2024-10-revealing-causal-links-complex-algorithm.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. --------------------------------------------------------------------- Explore further Research investigates variable star population of globular cluster NGC 1851 --------------------------------------------------------------------- 62 shares * Facebook * Twitter * Email Feedback to editors * Featured * Last Comments * Popular Virtual training uses generative AI to teach robots how to traverse real world terrain 2 hours ago 0 Creating compact near-sensor computing chips via 3D integration of 2D materials Nov 10, 2024 0 First practical application of viscous electron flow realizes terahertz photoconductivity in graphene Nov 9, 2024 0 One-step electrochemical regeneration of CO2 from (bi)carbonates enhances carbon capture efficiency Nov 8, 2024 0 Unique memristor design with analog switching shows promise for high-efficiency neuromorphic computing Nov 7, 2024 0 --------------------------------------------------------------------- [gif] Graph-based AI model finds hidden links between science and art to suggest novel materials 50 minutes ago [gif] Virtual training uses generative AI to teach robots how to traverse real world terrain 2 hours ago [gif] Waymo's robotaxis now open to anyone who wants a driverless ride in Los Angeles 5 hours ago [gif] Haptic hardware offers waterfall of immersive experience, could someday aid blind users 8 hours ago [gif] Giving robots superhuman vision using radio signals 8 hours ago [gif] AI-powered e-nose can detect oil spills efficiently Nov 11, 2024 [gif] Engineers capture octopus arm's intricate muscular architecture with an unprecedented computational model Nov 11, 2024 [gif] Discovery taps 'hot carriers' for on-demand, emissions-free hydrogen and catalyst regeneration Nov 11, 2024 [gif] Software package can bypass CPU for more efficient computing Nov 11, 2024 [gif] Carpet fibers can stop concrete cracking Nov 11, 2024 [INS::INS] * Related Stories [gif] Research investigates variable star population of globular cluster NGC 1851 Aug 28, 2024 [gif] Novel algorithm proposed for efficient selection of variables in chemometrics applications Aug 1, 2023 [gif] Two new variable stars detected in globular cluster NGC 6558 Jul 8, 2024 [gif] Astronomer detects eclipses in a candidate cataclysmic variable system Oct 16, 2024 [gif] Team applies variable reduction strategy to improve emergency material scheduling Jun 29, 2023 [gif] A framework to assess the importance of variables for different predictive models Jan 12, 2021 * Recommended for you [gif] Engineers capture octopus arm's intricate muscular architecture with an unprecedented computational model Nov 11, 2024 [gif] Carpet fibers can stop concrete cracking Nov 11, 2024 [gif] AI is universally bad at knowing when to chime in during a conversation: Researchers discover some of the root causes Nov 11, 2024 [gif] Wavy pipes prove to be more efficient than straight pipes in hybrid solar energy systems Nov 11, 2024 [gif] Artificial magnetic muscles can support tensile stresses up to 1,000 times their own weight Nov 8, 2024 [gif] Material with increased band gap design could make electronics faster and more efficient Nov 8, 2024 Load comments (1) Let us know if there is a problem with our content Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form. For general feedback, use the public comments section below (please adhere to guidelines). Please select the most appropriate category to facilitate processing of your request [-- please select one -- ] [ ] [ ] [ ] [ ] [ ] Your message to the editors [ ] Your email (only if you want to be contacted back) [ ] Send Feedback Thank you for taking time to provide your feedback to the editors. Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages. E-mail the story Revealing causal links in complex systems: New algorithm reveals hidden influences Your friend's email [ ] Your email [ ] [ ] I would like to subscribe to Science X Newsletter. Learn more Your name [ ] Note Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Tech Xplore in any form. [ ] [ ] [ ] [ ] [ ] [ ] [ ] Your message [ ] Send Phys.org logotype Phys.org Daily science news on research developments and the latest scientific innovations MedicalXpress logotype Medical Xpress Medical research advances and health news ScienceX logotype Science X The most comprehensive sci-tech news coverage on the web Newsletters [ ] Subscribe Science X Daily and the Weekly Email Newsletter are free features that allow you to receive your favorite sci-tech news updates in your email inbox Follow us * * * * * Top * Home * Search * Mobile version * Help * FAQ * About * Contact * Science X Account * Premium Account * Newsletter * Archive * Android app * iOS app * RSS feeds * Push notification (c) Tech Xplore 2014 - 2024 powered by Science X Network Privacy policy Terms of use Your Privacy This site uses cookies to assist with navigation, analyse your use of our services, collect data for ads personalisation and provide content from third parties. By using our site, you acknowledge that you have read and understand our Privacy Policy and Terms of Use. I'm OK with that Cookie options E-mail newsletter [ ] Subscribe Follow us * * * * It appears that you are currently using Ad Blocking software. What are the consequences? x Quantcast