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E-commerce companies have a number of online products, such as organicsearch, sponsored search, and recommendation modules, to fulfill customerneeds. Although each of these products provides a unique opportunity for usersto interact with a portion of the overall inventory, they are all similarchannels for users and compete for limited time and monetary budgets of users.To optimize users' overall experiences on an E-commerce platform, instead ofunderstanding and improving different products separately, it is important togain insights into the evidence that a change in one product would induce usersto change their behaviors in others, which may be due to the fact that theseproducts are functionally similar. In this paper, we introduce causal mediationanalysis as a formal statistical tool to reveal the underlying causalmechanisms. Existing literature provides little guidance on cases wheremultiple unmeasured causally-dependent mediators exist, which are common in A/Btests. We seek a novel approach to identify in those scenarios direct andindirect effects of the treatment. In the end, we demonstrate the effectivenessof the proposed method in data from Etsy's real A/B tests and shed lights oncomplex relationships between different products.

The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis