MSP quantifies the minimum changes to analyst choices required to falsify a causal claim by making its confidence interval contain zero, providing information orthogonal to dispersion-based robustness summaries.
Lalonde(1986)afternearlyfourdecades: Lessonslearned
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Measurement error in latent confounders produces biased ATE estimates and miscalibrated intervals under conventional adjustment; a Bayesian joint model of measurement, treatment, and outcome is proposed to correct it.
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
citing papers explorer
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Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference
MSP quantifies the minimum changes to analyst choices required to falsify a causal claim by making its confidence interval contain zero, providing information orthogonal to dispersion-based robustness summaries.
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Measurement Induced Confounding
Measurement error in latent confounders produces biased ATE estimates and miscalibrated intervals under conventional adjustment; a Bayesian joint model of measurement, treatment, and outcome is proposed to correct it.
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Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.