Rourkela : It seems impossible to find the causes of real-world problems in economics and public health . Often, multiple causes are suspected but large data sets with time-sequenced data are not available. Previously developed models could not reliably analyse these challenges but now researchers of the National Institute of Technology Rourkela and University of Johannesburg have tested the first Artificial Intelligence model that can tackle these problems.
When something bad happens to a person, it is human to try figure out why it happened. 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. To really know what caused an event, we need to look at causality: how information flows from one event to another but now in a huge Artificial Intelligence breakthrough, researchers Professor Snehashish Chakrabarty, Dr. Pramod Kumar Parida of NIT Rourkela, India and Prof. T. Marwala, University of Johannesburg, South Africa have developed the first robust model for general causality which identifies multiple causal connections without time-sequence data: a Multivariate Additive Noise Model (MANM).
We have a blind belief that if a black cat runs across the road or an owl hoots on a roof, some people may think that bad thing may happen. One can think that there is a connection between seeing the cat or the owl and bad may happen thereafter. We spoke to Prof Snehashish Chakraverty about this model he said, ” The Multivariate Additive Noise Model (MANM) provides significantly better causal analysis on real-world datasets than industry-standard models currently in use. 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 added . Professor Chakrabarty stated “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). 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,”concluded professor Chakraverty.
Dr Pramod Kumar Prida, the second author of the research said, ” The causes of persisting household debt are a good example of what the new model is capable of”. 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. ‘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 added.
He further said, “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,” said Parida. “With this model, you 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.” said Dr. Pramod Parida . Here the note worthy thing is that the model has also Dear been tested on simulated, real-world datasets. The research is published in the journal Neural Networks.
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