aiqbench analysis
This research introduces Minimum Specification Perturbation (MSP), a novel metric for assessing the robustness of causal inference by quantifying how many analyst decisions must change to falsify a result. Unlike traditional dispersion-based summaries, MSP captures distance-to-falsification information and demonstrates lower false-positive rates when dealing with weak effects in empirical studies. The work provides critical computational insights, distinguishing between tractable additive structures and NP-hard general cases for calibration under randomization.
Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference
arXiv:2605.01579v2 Announce Type: replace-cross Abstract: Empirical causal claims depend on many analyst decisions, from selecting covariates to choosing estimators. Existing robustness tools summarize how results vary across these choices, but, to the best of our knowledge, do not answer: \textbf{How many analyst decisions must change to reach a specification, which is a set of choices, whose confidence interval (CI) contains zero?} We introduce \emph{Minimum Specification Perturbation (MSP)}, the smallest number of changes. MSP is small under the null, grows with effect strength and captures distance-to-falsification information that dispersion-based summaries cannot report; when making decisions under weak effects, an MSP-based rule yields lower false-positive rates than dispersion-based rules. We show that Fragility Index and MSP measure orthogonal vulnerabilities: fragility to influential observations need not imply fragility to specification choices. On the LaLonde benchmark, MSP = 1 implies that one decision change makes the CI contain zero. We further provide exact permutation calibration under randomization and characterize computation, showing tractable cases under additive structure and NP-hardness in general.
Abstract:Empirical causal claims depend on many analyst decisions, from selecting covariates to choosing estimators. Existing robustness tools summarize how results vary across these choices, but, to the best of our knowledge, do not answer: \textbf{How many analyst decisions must change to reach a specification, which is a set of choices, whose confidence interval (CI) contains zero?} We introduce \emph{Minimum Specification Perturbation (MSP)}, the smallest number of changes. MSP is small under the null, grows with effect strength and captures distance-to-falsification information that dispersion-based summaries cannot report; when making decisions under weak effects, an MSP-based rule yields lower false-positive rates than dispersion-based rules. We show that Fragility Index and MSP measure orthogonal vulnerabilities: fragility to influential observations need not imply fragility to specification choices. On the LaLonde benchmark, MSP = 1 implies that one decision change makes the CI contain zero. We further provide exact permutation calibration under randomization and characterize computation, showing tractable cases under additive structure and NP-hardness in general.
Methodology (stat.ME); Machine Learning (cs.LG)
arXiv:2605.01579 [stat.ME]
(or arXiv:2605.01579v2 [stat.ME] for this version)
https://doi.org/10.48550/arXiv.2605.01579 Focus to learn more arXiv-issued DOI via DataCite
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