One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions such as emails, display ads, and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, most do not have any formal justification. The main contribution in this work is to propose an axiomatic framework for attribution in online advertising. The authors show that the most common heuristics can be cast under the framework and illustrate how these may fail. The authors propose a novel attribution metric, which they refer to as counterfactual adjusted Shapley value (CASV), which inherits the desirable properties of the traditional Shapley value while overcoming its shortcomings in the online advertising context. The authors also propose a Markovian model for the user journey through the conversion funnel, in which ad actions may have disparate impacts at different stages. The authors use the Markovian model to compare their metric with commonly used metrics. Furthermore, under the Markovian model, the authors establish that the CASV metric coincides with an adjusted “unique-uniform” attribution scheme. This scheme is efficiently implementable and can be interpreted as a correction to the commonly used uniform attribution scheme. The authors supplement their theoretical developments with numerical experiments using a real-world large-scale data set.