Causation vs. Correlation

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One of the most basic tenants of statistics is that correlation does not imply causation. In turn, a signal’s predictive power does not necessarily imply in any way that that signal is actually related to or explains the phenomena being predicted. This distinction matters when it comes to machine learning because many of the strongest signals these algorithms pick up in their training data are not actually related to the thing being measured. A Reminder That Machine Learning Is About Correlations Not Causation | Kalev Leetaru - Forbes

Getting to Causality

Multivariate Additive Noise Model (MANM)

Model for general causality that identifies multiple causal connections without time-sequence data. "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, a professor of artificial intelligence, and global AI and economics expert at the University of Johannesburg, South Africa. "The model is especially useful at the regional, national or global level where no controlled or natural experiments are possible," adds Marwala. "MANM is based on Directed Acyclic Graph (DAG), 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. Artificial intelligence trained to analyze causation | University of Johannesburg