1 citations · 1 across the 7 of their papers we have counts for
10 papers
Treatment Effect Modification by Latent Classes
Hui Lan, Nathan Kallus, Winston Chou
Many treatments have effects that plausibly vary by latent classes. For example, the effect of personalized video recommendations may be stronger for users who seek a new discovery…
Policy Learning with Weak Signals
Benedikt Koch, Winston Chou, Aurélien Bibaut +1
Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes. We formalize this regime by modelin…
Recommendation Quality and the Concentration of Consumption: Experimental Evidence from Netflix
Guy Aridor, Winston Chou, Nathan Kallus +3
We study an experiment with 8.5 million users on Netflix's recommender system to measure how improvements in recommendation technology affect the set of products that get consumed.…
Evaluating for the long term: Learnings from industry
Leif Sigerson, Tom Cunningham, Winston Chou +22
Online platforms prioritize long-term business outcomes, yet typical experiments are far too short to measure these outcomes directly. Our goal in this paper is to collect and shar…
A Human-Augmenting Agentic Workflow for Observational Causal Inference
Winston Chou, Adrien Alexandre, Lars Olds +2
Data analysis agents are becoming increasingly common tools for applied and scientific research. Yet, for highly specialized tasks such as Observational Causal Inference (OCI), hum…
Blending Proxy Metrics with a North Star
Winston Chou
Proxy metrics are widely used to improve the precision and velocity of online experimentation (aka A/B testing). Although proxies are often motivated by long-term outcomes that the…
The Value of Personalized Recommendations: Evidence from Netflix
Kevin Zielnicki, Guy Aridor, Aurélien Bibaut +3
Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challengin…
Estimating Representative Causal Effects with Double Machine Learning
Apoorva Lal, Winston Chou
Double Machine Learning is widely used to estimate treatment effects from non-experimental data. The "residuals-on-residuals" regression (RORR) is especially popular for its simpli…
Evaluating Decision Rules Across Many Weak Experiments
Winston Chou, Colin Gray, Nathan Kallus +2
Technology firms conduct randomized controlled experiments ("A/B tests") to learn which actions to take to improve business outcomes. In firms with mature experimentation platforms…
Learning the Covariance of Treatment Effects Across Many Weak Experiments
Aurélien Bibaut, Winston Chou, Simon Ejdemyr +1
When primary objectives are insensitive or delayed, experimenters may instead focus on proxy metrics derived from secondary outcomes. For example, technology companies often infer…