Mean independence restrictions are widely used to identify average treatment effects, but the covariates used to formulate these restrictions are often observed or used in coarsened form. This paper shows that mean independence conditional on a finely measured covariate need not hold after the covariate is replaced by a coarsened version. Coarsening can therefore change the identifying content of the comparison rather than only the precision of estimation. I characterize the resulting coarsening bias for the ATT and ATE. For the ATT, the bias is driven by within-cell composition in untreated potential outcomes. For the ATE, an additional treatment effect heterogeneity term appears. I then introduce $c$-mean dependence, a nonparametric relaxation of exact mean independence at the coarsened level, and derive sharp bounds for average treatment effects. The parameter $c$ determines how far comparisons may extend across neighboring values of the coarsened covariate. I propose sample analogue estimators and bootstrap inference procedures for the resulting bound functions. In an application to voluntary military service using data from Angrist (1998), positive effects on early earnings, which primarily reflect military pay, hold even under the weakest form of $c$-mean dependence, whereas the evidence on later effects on civilian earnings is inconclusive once mean independence is relaxed.

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