Shopping in the Multiverse: A Counterfactual Approach to In-Session
  Attribution
  We tackle the challenge of in-session attribution for on-site search engines
in eCommerce. We phrase the problem as a causal counterfactual inference, and
contrast the approach with rule-based systems from industry settings and
prediction models from the multi-touch attribution literature. We approach
counterfactuals in analogy with treatments in formal semantics, explicitly
modeling possible outcomes through alternative shopper timelines; in
particular, we propose to learn a generative browsing model over a target shop,
leveraging the latent space induced by prod2vec embeddings; we show how natural
language 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 important
patterns emerged in customer interviews and qualitative analysis, and we
present preliminary findings on an industry dataset from a partnering shop.
