Causal Inference
Causal inference is the process of drawing a conclusion about a causal relationship based on the evidence available. In data science, causal inference is used to determine whether a particular treatment or intervention has a causal effect on an outcome of interest. This involves identifying potential confounding variables that may affect both the treatment and the outcome, and using statistical methods to control for these variables. Causal inference is important in many areas of data science, including healthcare, economics, and social sciences, where it is often necessary to determine the effectiveness of a particular intervention or policy.
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