  Online experimentation platforms collect user feedback at low cost and large
scale. Some systems even support real-time or near real-time data processing,
and can update metrics and statistics continuously. Many commonly used metrics,
such as clicks and page views, can be observed without much delay. However,
many important signals can only be observed after several hours or days, with
noise adding up over the duration of the episode. When episodical outcomes
follow a complex sequence of user-product interactions, it is difficult to
understand which interactions lead to the final outcome. There is no obvious
attribution logic for us to associate a positive or negative outcome back to
the actions and choices we made at different times. This attribution logic is
critical to unlocking more targeted and efficient measurement at a finer
granularity that could eventually lead to the full capability of reinforcement
learning. In this paper, we borrow the idea of Causal Surrogacy to model a
long-term outcome using leading indicators that are incrementally observed and
apply it as the value function to track the progress towards the final outcome
and attribute incrementally to various user-product interaction steps. Applying
this approach to the guest booking metric at Airbnb resulted in significant
variance reductions of 50% to 85%, while aligning well with the booking metric
itself. Continuous attribution allows us to assign a utility score to each
product page-view, and this score can be flexibly further aggregated to a
variety of units of interest, such as searches and listings. We provide
multiple real-world applications of attribution to illustrate its versatility.
