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We tackle the challenge of in-session attribution for on-site search enginesin eCommerce. We phrase the problem as a causal counterfactual inference, andcontrast the approach with rule-based systems from industry settings andprediction models from the multi-touch attribution literature. We approachcounterfactuals in analogy with treatments in formal semantics, explicitlymodeling possible outcomes through alternative shopper timelines; inparticular, we propose to learn a generative browsing model over a target shop,leveraging the latent space induced by prod2vec embeddings; we show how naturallanguage queries can be effectively represented in the same space and how"search intervention" can be performed to assess causal contribution. Finally,we validate the methodology on a synthetic dataset, mimicking importantpatterns emerged in customer interviews and qualitative analysis, and wepresent preliminary findings on an industry dataset from a partnering shop.

Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution