This paper examines robustness and causal interpretation of panel estimates using double/debiased machine learning. The prototype treatment setup is the 2018 US banking regulation. Under common treatment timing, we characterize identification and nuisance restrictions and derive exact bank-level representations of specified "within" and "first-difference" scores. Simulations assess panel model transformation-induced specification bias and inference under weaker overlap. Empirically, estimates vary with asset-size classification and prior growth. Prior-growth adjustment reduces overlap-weighted estimates across four Machine Learners (ML); doubly robust estimates depend on the fitted propensity and concentrated weights. Both bank groups received relief, so we estimate differential exposure effects. The findings highlight specification issues and the relation between functional-form robustness and causal identification.

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