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arXiv Machine Learning By Hoang Dang, Luan Pham, Minh Nguyen

Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference

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The paper introduces Minimum Specification Perturbation (MSP), a metric that counts the smallest number of analyst decisions that must be altered to make a causal study’s confidence interval include zero. MSP is small under the null hypothesis, grows with effect size, and provides a distance‑to‑falsification measure that traditional dispersion‑based robustness tools cannot capture. The authors demonstrate that MSP and the Fragility Index assess different vulnerabilities, and show that on the LaLonde benchmark MSP equals one, meaning a single decision change would render the estimate statistically insignificant.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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