We develop SHOPPER, a sequential probabilistic model of shopping data.\nSHOPPER uses interpretable components to model the forces that drive how a\ncustomer chooses products; in particular, we designed SHOPPER to capture how\nitems interact with other items. We develop an efficient posterior inference\nalgorithm to estimate these forces from large-scale data, and we analyze a\nlarge dataset from a major chain grocery store. We are interested in answering\ncounterfactual queries about changes in prices. We found that SHOPPER provides\naccurate predictions even under price interventions, and that it helps identify\ncomplementary and substitutable pairs of products.\n