KDD 2024 / Paper
Sensitivity Analysis for Causal ML: A Use Case at Booking.com
A practical framework for stress-testing causal claims when unobserved confounders cannot be ruled out — illustrated on a real Booking.com problem.
PERSONAL SITE / UPDATED 2026.05
Lin Jia.
Senior Data Scientist & Craft Lead
/ Booking.com
/ Amsterdam
9+ years across statistical inference, causal inference, machine learning, and GenAI evaluation. I lead initiatives that combine statistical rigor with strategic impact — from a company-wide Causal Inference Framework to sequential testing, causal ML, and LLM-powered experimentation intelligence.
CAUSAL INFERENCE
EXPERIMENTATION
GENAI TOOLING
MACHINE LEARNING
BOOKING.AI / Article
The Causality Gap: Measuring the True Impact of Voluntary Adoption
In digital marketplaces where adoption is voluntary, naïve comparisons confound self-selection with the treatment effect. A causal framework for closing that gap across demand and supply.
BOOKING EXP CONF / May 2026
Panel: AI in Experimentation
Panel discussion on how GenAI is reshaping experimentation — copilots, memory, evaluation, trust, and the trade-offs that remain fundamentally human.
My home for building trustworthy, decision-ready thinking across statistical inference, causal inference, machine learning, and GenAI evaluation. Three series, one shared standard for rigor.
SERIES 01
Causal Inference From the Ground Up
Foundations: identification, key assumptions, and a step-by-step model for any causal question.
7 episodes · ongoing
SERIES 02
Inference in the Wild
Messy data, imperfect experiments, evaluation pitfalls, and playbooks that hold up under real-world constraints.
2 posts · ongoing
SERIES 03
Field Notes
Shorter dispatches from working in the data-science trenches — currently exploring GenAI literacy for ICs.
2 episodes · ongoing