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Causal Estimation of User Learning in Personalized Systems

Author

Listed:
  • Evan Munro
  • David Jones
  • Jennifer Brennan
  • Roland Nelet
  • Vahab Mirrokni
  • Jean Pouget-Abadie

Abstract

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.

Suggested Citation

  • Evan Munro & David Jones & Jennifer Brennan & Roland Nelet & Vahab Mirrokni & Jean Pouget-Abadie, 2023. "Causal Estimation of User Learning in Personalized Systems," Papers 2306.00485, arXiv.org.
    • Handle: RePEc:arx:papers:2306.00485
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    References listed on IDEAS

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    Cited by:

    1. Shan Huang & Chen Wang & Yuan Yuan & Jinglong Zhao & Jingjing Zhang, 2023. "Estimating Effects of Long-Term Treatments," Papers 2308.08152, arXiv.org.

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