The article introduces the use of Directed Acyclic Graphs (DAG) and backdoor criterion in causal inference for experimental settings to select good control variables. It explains the process through a data science problem of influencing sustainable behavior and includes examples and simulated experiments in R to demonstrate the application. The article emphasizes the importance of selecting valid control variables for accurate causal analysis.
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How to Use Backdoor Criterion to Select Control Variables
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