# Causal Estimation of User Learning in Personalized Systems

arXiv: https://arxiv.org/abs/2306.00485 · HTML: https://mlsanity.com/paper/2306.00485/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [Design and analysis of experiments in networks: Reducing bias from interference](https://mlsanity.com/paper/1404.7530/) (2014)
- [Experimental Design in Two-Sided Platforms: An Analysis of Bias](https://mlsanity.com/paper/2002.05670/) (2020)
- [Design and Analysis of Switchback Experiments](https://mlsanity.com/paper/2009.00148/) (2020)
- [Time series experiments and causal estimands: exact randomization tests and trading](https://mlsanity.com/paper/1706.07840/) (2017)
- [Multiple Randomization Designs: Estimation and Inference with Interference](https://mlsanity.com/paper/2112.13495/) (2021)
- [Treatment Effects in Market Equilibrium](https://mlsanity.com/paper/2109.11647/) (2021)
- [Rate-Optimal Cluster-Randomized Designs for Spatial Interference](https://mlsanity.com/paper/2111.04219/) (2021)
- [Experimental Design under Network Interference](https://mlsanity.com/paper/2003.08421/) (2020)
- [Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Model](https://mlsanity.com/paper/2103.06392/) (2021)
- [Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective](https://mlsanity.com/paper/2003.09915/) (2020)
- [Novelty and Primacy: A Long-Term Estimator for Online Experiments](https://mlsanity.com/paper/2102.12893/) (2021)
- [Distributed Balanced Partitioning via Linear Embedding](https://mlsanity.com/paper/1512.02727/) (2015)

## Abstract (arXiv)

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.