Causal Estimation of User Learning in Personalized Systems
This addresses measurement challenges for online platforms in evaluating long-term treatment impacts, but it is incremental as it builds on existing causal models.
The paper tackles the problem of biased causal estimation in personalized systems due to user learning and system personalization, showing that new experimental designs can recover dynamic causal effects in simulations.
In online platforms, the impact of a treatment on an observed outcome may change over time as 1) users learn about the intervention, and 2) the system personalization, such as individualized recommendations, change over time. We introduce a non-parametric causal model of user actions in a personalized system. We show that the Cookie-Cookie-Day (CCD) experiment, designed for the measurement of the user learning effect, is biased when there is personalization. We derive new experimental designs that intervene in the personalization system to generate the variation necessary to separately identify the causal effect mediated through user learning and personalization. Making parametric assumptions allows for the estimation of long-term causal effects based on medium-term experiments. In simulations, we show that our new designs successfully recover the dynamic causal effects of interest.