NOVEMBER 5, 2020 with Causal Reasoning AI Augmenting Analytics · Augmenting Analytics with Causal...

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Transcript of NOVEMBER 5, 2020 with Causal Reasoning AI Augmenting Analytics · Augmenting Analytics with Causal...

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    MIDWEST ARCHITECTURE COMMUNITY COLLABORATION 2020NOVEMBER 5, 2020

    Augmenting Analytics with Causal Reasoning AI

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    WHO NEEDS CAUSAL REASONING AI

    ◼ Most systems can’t tell you why…Human brains are needed to hypothesize, experiment and conclude

    ◼ Answering why and how questions is often critical to better decisions

    ◼ Why did sales slump in region R at time T?◼ Why did disease spread jump in region R at time T?◼ Given factors X and Y, what are likely outcomes?

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    LIMITATIONS OF DESCRIPTIVE BI

    ◼ "That which is measured improves. That which is measured and reported improves exponentially." ~ Karl Pearson

    ◼ “Correlation does not imply causation." ~ Karl Pearson◼ "We must be careful not to confuse data with the

    abstractions we use to analyze them." ~ William James◼ “…with no antecedent knowledge of the causation or

    absence of causation … the calculation of correlation coefficients, total or partial, will not advance us a step toward evaluating the importance of the causes at work." ~ R. A. Fisher

    ◼ “Data can tell you that the people who took a medicine recovered faster than those who did not take it, but they can’t tell you why.” ~ Judea Pearl

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    AUGMENTING BI TOOLS IS THE KEY

    ◼ BI Tools are great at correlation and filtering, sorting and visualizing

    But they are best with structured data

    ◼ Content management tools are great at managing unstructured information

    But they do not support BI/Analytics

    ◼ We need tools to sit in the middle

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    ONTOLOGIES, ARISTOTLE AND THE SEMANTIC WEB

    ◼ Doug Lenat and Tim Berners-Lee championed the Semantic Web (W3C)

    ◼ OWL/RDF is great for tagging web content, but not enough for:

    Complex causal pathsDeep language understandingEffectively marrying structured and unstructured content

    ◼ The seeds of the solution were already sprouting in Aristotle’s time

    5ResearhGate

    https://www.w3.org/standards/semanticweb/https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.researchgate.net%2Ffigure%2FCausal-Loop-Diagram-shows-the-causal-diagram-of-the-whole-system-with-interactions_fig5_317402144&psig=AOvVaw3XJ9EvDBqKfPamUDlikvf7&ust=1604498782545000&source=images&cd=vfe&ved=0CAMQjB1qFwoTCICnnvTF5uwCFQAAAAAdAAAAABAc

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    INFORMATION IS NOT ENOUGH TO DETERMINE CAUSE

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    ◼ Data is flat, and not enough to discover correlations

    Information is needed◼ Relational models have more dimensions than flat models…◼ but not enough dimensions to efficiently discover causes

    Knowledge is needed◼ Graph structured knowledge can support causal reasoning

    ◼ Causal Paths are core to causal reasoningNecessary but not sufficient

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    UNDERSTANDING CAUSAL PATHS

    The diagram at right shows a causal network with multiple paths from tropical medicine studies on malaria

    ◼ The nodes are abbreviated, but understandable

    ◼ This shows “Confounders” in Age, Sequestration and Hypoglycemia

    ◼ It shows “Colliders” leading to several preliminary conditions and ultimately to Death

    ◼ It only tells the negative, omitting positive impacts of therapies or other conditions that lead to healing

    ◼ It doesn’t show external influences such as congenital or acquired Hypoglycemia or other comorbidities

    ◼ Even incomplete, it is extremely useful

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    https://www.tropmedres.ac/team/james-watson/

    https://www.tropmedres.ac/team/james-watson/

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    KNOWLEDGE DIMENSIONS

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    Concept data 🡪 Concept graph◼ Causal data is needed to associate causes

    with possible outcomes: Presuppositions◼ Taxonomical data is needed for inheritance◼ Language data is needed for:

    Concept LearningCausal Path ConstructionSolution Validation

    ◼ Time, location and other context for NLU

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    CHALLENGES TO CAUSAL INFERENCE

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    ◼ Confounders and Colliders make causal inference more challenging

    ◼ Causal knowledge alone is often not enough◼ Taxonomical, meronomical and space/time

    knowledge often serve critical roles◼ The amount of concept knowledge needed

    is huge except for narrow domains◼ Causal inference paths may be chaotic

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    COLLIDERS

    ◼ Some outcomes have many contributing factors

    ◼ Both better education and diet improve health

    ◼ It may be that better education affects diet making it both a direct and intermediate cause

    ◼ Root causes may exist a-priori Education may be impacted by location, prosperity or cultural factors

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    Better Education

    Better HealthBetter Income

    Higher Taxes Better Diet

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    CONFOUNDERS

    ◼ One cause or causal factor may have many possible outcomes

    Better education may improve several outcomes

    ◼ Causality also involves detractorsHigher taxes could impair a person’s sense of well-being

    ▪ Computational models must support multiple modes of inference

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    Better Education

    Better HealthBetter Income

    Higher Taxes Better Diet

    Sense of Well-being

    Economic Prosperity

    Cultural Emphasis

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    THREE SOURCES

    ◼ Transactional data can provide input cases◼ Hypothetical models support definition of

    presuppositions◼ Historical Cases support both a-priori

    learning and a-posteriori validation◼ Causal inference traverse paths so all

    three sources are best structured to support path navigation

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    Transactional Data

    Historical CasesHypothetical Model

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    RESOLUTION OF DEIXIS

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    ◼ Deixis, context and pragmatics are deep layers of natural language (NL)

    ◼ Presuppositions about what “can” happen can show what did happen or will happen

    ◼ Exformation is often needed for resolutionCharacteristics of the subject like shapeActions that can apply to the subject

    ◼ Logical syllogisms are key to inference

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    HYPOTHETICAL MODELS AND PRESUPPOSITION

    ◼ Simple hypothetical models are usually naïve and unreliable

    ◼ The more model data you can have, the better the inference

    ◼ Data mining requires some NL◼ The more powerful the NLU

    the more accurate model data you can gather

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    RESOURCES

    ◼ The Book of Why: Judea Pearl, Basic 2018◼ Wolfram Physics◼ Pragmatics: Yan Huang, Oxford 2007◼ The Semantic Web: Tim Berners-Lee, 2001◼ The Grammar of Science: Karl Pearson, Black 1892◼ Metaphysics: Aristotle, Self-Published 340 BC

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    https://www.wolframphysics.org/technical-introduction/the-updating-process-for-string-substitution-systems/the-relationship-between-graphs-and-the-multiway-causal-graph/https://www.researchgate.net/publication/307845029_Tim_Berners-Lee's_Semantic_Webhttps://plato.stanford.edu/entries/aristotle-metaphysics/

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