Assessing covariate balance is central to causal inference, but commonly used summary measures may miss distributional differences. I develop weighted extensions of the Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Cramer-von Mises (CVM) statistics and investigate their calibration when weights are externally supplied or estimated from treatment assignment. With exogenous weights, fixed-weight permutation produced approximately nominal Type I error. With estimated propensity-score weights, fixed-weight permutation was generally conservative, whereas re-estimating weights after unrestricted permutation could be substantially anticonservative when observed and resampled weighting regimes differed. Assignment-mechanism resampling was generally conservative under correct propensity-model specification but could become anticonservative under misspecification. Among the approaches examined for estimated propensity-score weights, fixed-weight permutation therefore provided the most reliable calibration, although it was generally conservative. AD retained strong sensitivity to tail discrepancies, while AD and CVM outperformed KS for diffuse discrepancies. For central discrepancies, KS was most powerful with exogenous weights, whereas AD was most powerful under estimated weighting. An inverse-probability-weighting example illustrates how these tests complement standardized mean differences and variance ratios. Weighted EDF tests provide complementary whole-distribution balance diagnostics. The methods are implemented in the Stata commands kstest, adtest, and cvmtest.

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