Nav: Home

Beyond superstition to general causality: AI nutcracker for real-world problems

June 05, 2018

When something bad happens to a person, it is human to try figure out why it happened. What caused it? If we understand that, it may be possible to avoid the same outcome the next time. However, some of the ways that we use to try to understand events, such as superstition, cannot explain what is actually going on. Neither does correlation - which can only say that event B happened round about the same time as event A.

To really know what caused an event, we need to look at causality: how information flows from one event to another. It is the information flow that shows there is a causal link - that event A caused event B. But what happens when the time-sequenced information flow from event A to event B is missing? Now we need general causality to identify the causes.

General nutcracker

Mathematical models for general causality have been very limited, working for up to two causes. Now in a huge Artificial Intelligence breakthrough, researchers have developed the first robust model for general causality which identifies multiple causal connections without time-sequence data: a Multivariate Additive Noise Model (MANM).

Researchers from the University of Johannesburg, South Africa and National Institute of Technology Rourkela, India developed the model and tested it on simulated, real-world datasets. The research is published in the journal Neural Networks.

"Uniquely, the model can identify multiple, hierarchical causal factors. It works even if data with time sequencing is not available. The model creates significant opportunities to analyse complex phenomena in areas such as economics, disease outbreaks, climate change and conservation," says Prof Tshilidzi Marwala, Professor of Artificial Intelligence (AI), and global AI and Economics expert at the University of Johannesburg, South Africa.

"The model is especially useful at regional, national or global level where no controlled or natural experiments are possible," adds Marwala.

Superstition and correlation towards causality

"If a black cat runs across the road, or an owl hoots on a roof, some people are convinced something really bad is going to happen. A person can think there is a connection between seeing the cat or the owl and what happened afterwards. However, from an Artificial Intelligence point of view, we say there are no causal links between the cat, the owl, and what happens to the people who see them. The cat or the owl were seen just before the event, but they are merely correlated in time with what happened later," says Prof Marwala.

Meanwhile, back in the house that the owl sat on, something more sinister may be going on. The family inside may be sliding deeper and deeper into debt. Such a financial situation can impose more and more severe restrictions on the household, eventually becoming a trap from which there can be little escape. But do the people living there understand why - what the actual causal connections are between what happens to them, what they do and their levels of debt?

Causality at household level

The causes of persisting household debt are a good example of what the new model is capable of, says post-doctoral researcher Dr Pramod Kumar Parida, lead author of the research article.

"At a household level one can ask: Has the household lost some or all of its income? Are some or all members spending beyond their income? Has something happened to household members that is forcing huge spend, such as medical or disability bills? Are they using up their savings or investments, which have now run out? Is a combination of these things happening, if so, which are the more dominant causes of the debt?"

If enough data about the household's financial transactions is available, complete with date and time information, it is possible for someone to figure out the actual causal connections between income, spend, savings, investments and debt.

In this case, simple causality theory is sufficient to find out why this household is struggling.

General causality at societal level

But says Parida, if one asks: 'What are the real reasons most people in a city or a region are struggling financially? Why are they not getting out of debt?' Now it is no longer possible for a team of people to figure this out from available data. Now a whole new mathematical challenge opens up.

"Especially if you want the actual causal connections between household income, spend, savings and debt for the city or region, rather than expert guesses or 'what most people believe'," he adds.

"Here, causality theory doesn't work anymore, because the financial transaction data for households in the city or region will be incomplete. Also, date and time information will be missing on some data. Financial struggle in low, middle and high-income households may be very different, so you'll want to see the different causes from the analysis," says Parida.

"With this model, you can identify can identify multiple major driving factors causing the household debt. In the model, we call these factors the independent parent causal connections. You can also see which causal connections are more dominant than the others. With a second pass through the data, you can also see the minor driving factors, what we call the independent child causal connections. In this way, it is possible to identify a possible hierarchy of causal connections."

Significantly improved causal analysis

The Multivariate Additive Noise Model (MANM) provides significantly better causal analysis on real-world datasets than industry-standard models currently in use, says co-author Prof Snehashish Chakraverty, at the Applied Mathematics Group, Department of Mathematics, National Institute of Technology Rourkela, India.

"In order to improve a complex regional problem such as household debt or healthcare challenges, it may not be sufficient to have the knowledge of patterns of the debt, or of disease and the exposure. On the contrary, we should understand why such patterns exist, to have the best way of changing them. Previous models developed by researchers worked with a maximum of two causal factors, that is they were bivariate models, which simply could not find multiple feature dependency criteria," he says.

Directed Acyclic Graphs

"MANM is based on Directed Acyclic Graphs (DAGs), which can identify a multi-nodal causal structure. MANM can estimate every possible causal direction in complex feature sets, with no missing or wrong directions."

The use of DAGs is a key reason MANM significantly outperforms models previously developed by others, which were based on Independent Component Analysis (ICA), such as Linear Non-Gaussian Acyclic Model (ICA-LiNGAM), Greedy DAG Search (GDS) and Regression with Sub-sequent Independent Test (RESIT), he says.

"Another key feature of MANM is the proposed Causal Influence Factor (CIF), for the successful discovery of causal directions in the multivariate system. The CIF score provides a reliable indicator of the quality of the casual inference, which enables avoiding most of the missing or wrong directions in the resulting causal structure," concludes Chakraverty.

Where an existing dataset is available, MANM now makes it possible to identify multiple multi-nodal causal structures within the set. As an example, MANM can identify the multiple causes of persistent household debt for low, middle and high-income households in a region.
The multimedia press pack is available for download, without registrations or logins, at

University of Johannesburg

Related Artificial Intelligence Articles:

Researchers rebuild the bridge between neuroscience and artificial intelligence
In an article in the journal Scientific Reports, researchers reveal that they have successfully rebuilt the bridge between experimental neuroscience and advanced artificial intelligence learning algorithms.
Artificial intelligence can help some businesses but may not work for others
The temptation for businesses to use artificial intelligence and other technology to improve performance, drive down labor costs, and better the bottom line is understandable.
Artificial intelligence could help predict future diabetes cases
A type of artificial intelligence called machine learning can help predict which patients will develop diabetes, according to an ENDO 2020 abstract that will be published in a special supplemental section of the Journal of the Endocrine Society.
Artificial intelligence for very young brains
Montreal's CHU Sainte-Justine children's hospital and the ÉTS engineering school pool their expertise to develop an innovative new technology for the segmentation of neonatal brain images.
Putting artificial intelligence to work in the lab
An Australian-German collaboration has demonstrated fully-autonomous SPM operation, applying artificial intelligence and deep learning to remove the need for constant human supervision.
Composing new proteins with artificial intelligence
Scientists have long studied how to improve proteins or design new ones.
Artificial intelligence and family medicine: Better together
Researcher at the University of Houston are encouraging family medicine physicians to actively engage in the development and evolution of artificial intelligence to open new horizons that make AI more effective, equitable and pervasive.
Artificial Intelligence to improve the precision of mammograms
The Artificial Intelligence techniques, used in combination with evaluations by expert radiologists, improve the precision in the detection of cancer through mammograms.
Using artificial intelligence to assess ulcerative colitis
Researchers from Tokyo Medical and Dental University (TMDU) have developed an artificial intelligence system with a deep neural network that can effectively evaluate endoscopic data from patients with ulcerative colitis, which is a type of inflammatory bowel disease, without the need for biopsy collection.
Robot uses artificial intelligence and imaging to draw blood
Rutgers engineers have created a tabletop device that combines a robot, artificial intelligence and near-infrared and ultrasound imaging to draw blood or insert catheters to deliver fluids and drugs.
More Artificial Intelligence News and Artificial Intelligence Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Climate Mindset
In the past few months, human beings have come together to fight a global threat. This hour, TED speakers explore how our response can be the catalyst to fight another global crisis: climate change. Guests include political strategist Tom Rivett-Carnac, diplomat Christiana Figueres, climate justice activist Xiye Bastida, and writer, illustrator, and artist Oliver Jeffers.
Now Playing: Science for the People

#562 Superbug to Bedside
By now we're all good and scared about antibiotic resistance, one of the many things coming to get us all. But there's good news, sort of. News antibiotics are coming out! How do they get tested? What does that kind of a trial look like and how does it happen? Host Bethany Brookeshire talks with Matt McCarthy, author of "Superbugs: The Race to Stop an Epidemic", about the ins and outs of testing a new antibiotic in the hospital.
Now Playing: Radiolab

Speedy Beet
There are few musical moments more well-worn than the first four notes of Beethoven's Fifth Symphony. But in this short, we find out that Beethoven might have made a last-ditch effort to keep his music from ever feeling familiar, to keep pushing his listeners to a kind of psychological limit. Big thanks to our Brooklyn Philharmonic musicians: Deborah Buck and Suzy Perelman on violin, Arash Amini on cello, and Ah Ling Neu on viola. And check out The First Four Notes, Matthew Guerrieri's book on Beethoven's Fifth. Support Radiolab today at