Instruction: Explain the purpose of sensitivity analysis and how it can be implemented in causal inference.
Context: This question evaluates the candidate's knowledge of sensitivity analysis as a method to test the robustness of the causal inferences against potential unmeasured confounding.
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At its core, the purpose of sensitivity analysis in causal inference is to evaluate how changes in our model's assumptions might affect our conclusions regarding the causal relationship between variables. This is particularly important because, in real-world data, we often deal with the possibility of unmeasured confounding—where variables that influence both the treatment and the outcome are not included in our model. Sensitivity analysis helps in identifying the degree to which our causal estimates could be biased due to these unmeasured confounders.
Implementing sensitivity analysis can be approached...