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This paper introduces the $f$-sensitivity model, a new sensitivity model that characterizes the violation of unconfoundedness in causal inference.
Counterfactual sensitivity and robustness
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The f-sensitivity index
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Dual and simultaneous sensitivity analysis for matched pairs
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On the informativeness of descriptive statistics for structural estimates
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
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Sharp sensitivity analysis for inverse propensity weighting via quantile balancing
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Doubly-valid/doubly-sharp sensitivity analysis for causal inference with unmeasured confounding
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Covariate balancing sensitivity analysis for extrapolating randomized trials across locations
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