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    Home»Artificial Intelligence»Propensity-Score Matching Is the Bedrock of Causal Inference | by Ari Joury, PhD | Dec, 2024
    Artificial Intelligence

    Propensity-Score Matching Is the Bedrock of Causal Inference | by Ari Joury, PhD | Dec, 2024

    Team_AIBS NewsBy Team_AIBS NewsDecember 22, 2024No Comments2 Mins Read
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    OPINION

    And how you can get began with it utilizing Python

    Towards Data Science

    Can coaching applications trigger extra financial prosperity? Picture generated with Leonardo AI

    A lot of up to date information science solutions the query “What’s occurring?” At my agency, for instance, we regularly attempt to spot how properly an organization is performing, and the way one efficiency indicator is tied to a different by way of correlations.

    A extra highly effective query price answering can be “Why is that this taking place?” For instance, if we detect a major correlation between the presence of ladies in administration and an organization’s revenues, what’s trigger and what’s impact right here? Or, if folks bear a coaching program, will this trigger their efficiency to enhance? Or would better-performing folks need to bear a coaching program, and therefore we solely see an impact as a result of choice bias?

    Several approaches exist to pinpoint causal relationships in information science. Propensity-Rating Matching (PSM) is without doubt one of the older ones, having emerged around 40 years ago. Different strategies like Structural Equation Modeling arose on the identical time. Approaches like Instrumental Variables arose a number of many years earlier than. Causal statistics continues to be a really lively area, with many new strategies being developed.

    A key benefit of PSM is that it permits researchers to work with real-world information. Specifically, it…



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