Minimax causal inference under tail heterogeneity

Omid Ardakani ()
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Omid Ardakani: Georgia Southern University

Economics Bulletin, 2026, vol. 46, issue 3, 1390 - 1391

Abstract: Standard causal estimators deteriorate when treatment modifies the tail index of outcomes. I generalize the maximum domain of attraction to nonstationary triangular arrays, establishing uniform convergence of block maxima to time-varying generalized extreme value distributions. Under such shifts, the supports of the limiting distributions separate, likelihood ratios are unbounded, and inverse probability weighting loses root-n consistency and asymptotic normality once the heavier tail index exceeds one half. I derive minimax lower bounds for the causal estimation rate; a tail-adaptive doubly robust estimator attains them up to logarithms. In simulations, tail adaptation cuts integrated mean absolute error by 20 to 41 percent at crisis-scale samples and stabilizes performance under strong tail heterogeneity. An application to bank stock returns around the 2020 Federal Reserve emergency facilities illustrates the framework.

Keywords: Extreme value theory; causal inference; minimax bounds; heavy tails; financial crises (search for similar items in EconPapers)
JEL-codes: C1 C2 (search for similar items in EconPapers)
Date: 2026-09-30
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