Allen Tran

I'm a Principal Machine Learning Scientist at Netflix. My work sits at the intersection of reinforcement learning, causal inference, and behavioral/preference modeling — building rigorous, bottom-up models of how people actually make decisions, and shipping them at scale.

Research

• scalable multi-task inverse reinforcement learning (under review, 2026)
• recommendation quality and the concentration of consumption: experimental evidence from Netflix (working paper, 2026)
• the value of personalized recommendations: evidence from Netflix (EC 2026; International Journal of Industrial Organization, 2026)
• semiparametric double reinforcement learning with applications to long-term causal inference (arXiv, 2025)
• inferring the long-term causal effects of long-term treatments from short-term experiments (ACIC 2024, ICML 2024 oral, ~top 1% of submissions)
• beyond customer lifetime valuation: measuring the value of acquisition and retention for subscription services (WebConf 2022)
• deep learning for driving detection on mobile phones (KDD 2016)

Other

• causal inference on observational studies @ Hulu (slides)
• determining and displaying a route using information from a vehicle, user feedback, and a mobile device (US Patent 10,036,639, 2018)

Fun proofs (I wish they were my proofs, they are just my notes!)

• Dinitz / Graph Coloring Proof from "The Book"
• 6 proofs of the Infinitude of Primes from "The Book"

Older side projects and notes on other people's papers (2014–16) are in the archive.

My earlier economics research from my Econ PhD at UCLA is here.