Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution
This addresses attribution challenges for eCommerce platforms, but it appears incremental as it builds on existing causal and embedding methods.
The paper tackles in-session attribution for eCommerce search engines by framing it as a causal counterfactual inference problem, using a generative browsing model with prod2vec embeddings to assess search interventions, and validates the approach on synthetic and industry datasets with preliminary findings.
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.