Tag: Sensitivity Analysis
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Causal Inference
Causal inference provides a framework for deducing the relationship between cause and effect using empirical data. It employs a variety of rigorous methods to ensure the validity of its findings, making it indispensable in fields such as policy evaluation, economics, and healthcare.
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Sensitivity Analysis
Sensitivity Analysis quantifies the impact of variable changes on a specific outcome within a model. Employed across various disciplines, it aids in risk assessment, model validation, and decision-making, offering metrics to represent sensitivity.
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Monte Carlo Simulation
Utilizing statistical sampling, Monte Carlo Simulation offers insights into risk and uncertainty within predictive models. By generating a spectrum of outcomes from random inputs, it finds applications in fields like finance and engineering. Its precision is contingent on the accuracy of the underlying model and inputs.