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| Omid M Ardakani |
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| ''Minimax causal inference under tail heterogeneity'' |
| ( 2026, Vol. 0 No.0 ) |
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| 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. |
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| Keywords: Extreme value theory, causal inference, minimax bounds, heavy tails, financial crises |
JEL: C1 - Econometric and Statistical Methods: General
C2 - Single Equation Models; Single Variables: General |
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| Manuscript Received : Apr 21 2026 | | Manuscript Accepted : Oct 10 2026 |
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