Learner, Practitioner & Teacher
Explainable AI (XAI) refers to a set of techniques and approaches used to help humans understand how machine learning and artificial intelligence systems make decisions. There are two main types of XAI methods: model-agnostic and model-specific. In this blog post,
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NEED Explainable AI (XAI) refers to the development of AI systems that can provide explanations for their decision-making processes. The importance of XAI is increasing as AI systems become more prevalent in various fields. In order to achieve XAI, several
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NEED Explainability is a key factor in ensuring that healthcare applications are safe, reliable, and trustworthy. In an industry where decisions can have life-altering consequences for patients, it is essential that algorithms and other technologies used in healthcare are transparent
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NEED Uplift modeling is a technique used in predictive analytics to estimate the impact of a treatment or intervention on an outcome. It involves predicting the individual treatment effect (ITE) for each individual in the dataset, and then using these
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What is Uplift Models Uplift modeling, also known as incremental modeling or true lift modeling, is a machine learning technique used to estimate the individual treatment effect of an intervention, such as a marketing campaign or medical treatment, on a
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Introduction: In recent years, the healthcare and pharmaceutical sectors have been increasingly turning to Real World Evidence (RWE) to complement traditional clinical trial data. RWE, derived from Real World Data (RWD), offers a broader perspective on how treatments work in
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Introduction The landscape of data analytics is undergoing a seismic shift. Gone are the days when mere proficiency in data manipulation and software skills sufficed. As we transition from Data Analyst 1.0 to Data Analyst 2.0, a new, more dynamic
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Here are the general steps for implementing propensity score matching to estimate treatment effects in an observational study: Define the research question: Start by defining the research question you want to answer. What is the treatment you want to evaluate?
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NEED Propensity score matching is a statistical technique commonly used in observational studies to estimate treatment effects. In an observational study, researchers cannot control which patients receive treatment and which do not. Therefore, they must use statistical methods to adjust
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Causal effect refers to the relationship between two variables, where one variable (the cause) influences the other variable (the effect). The causal effect measures the impact of the cause on the effect, controlling for other possible factors that may influence
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