# doubleml

DoubleML is a Python and R package implementing the double/debiased machine learning framework, with functionality for estimating models and performing statistical inference.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces

DevFeed: [The Causality Gap: Measuring the True Impact of Voluntary Adoption in Digital Marketplaces](<https://devfeed.tech/articles/the-causality-gap-measuring-the-true-impact-of-voluntary-adoption-in-digital-marketplaces-30456.md>)

Original publisher: [Read original article](<https://booking.ai/the-causality-gap-measuring-the-true-impact-of-voluntary-adoption-in-digital-marketplaces-ea68b5a35120?source=rss----4d265f07defc---4>)

Author: Lin Jia

Published: 2026-05-22T08:05:43Z

Content type: article

Language: en

Sources: [Booking.com Data Science](<https://devfeed.tech/sources/booking-com-data-science.md>)

Topics: [A/B Testing](<https://devfeed.tech/topics/a-b-testing.md>), [doubleml](<https://devfeed.tech/topics/doubleml.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [Tool](<https://devfeed.tech/topics/tool.md>)

Tags: [a-b-testing](<https://devfeed.tech/tags/a-b-testing.md>), [causal-inference](<https://devfeed.tech/tags/causal-inference.md>), [causal-machine-learning](<https://devfeed.tech/tags/causal-machine-learning.md>), [causality](<https://devfeed.tech/tags/causality.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [doubleml](<https://devfeed.tech/tags/doubleml.md>), [e-commerce](<https://devfeed.tech/tags/e-commerce.md>), [experimentation](<https://devfeed.tech/tags/experimentation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [measuring](<https://devfeed.tech/tags/measuring.md>)

### AI overview

This article explains why standard A/B tests can mismeasure features that require voluntary adoption in digital marketplaces. It presents Randomized Encouragement Design combined with Double Machine Learning as a way to estimate both causal impact among adopters and overall rollout impact.

### Source excerpt

by Lin Jia, Kexin Fei The Content of this post has been presented at Pydata Amsterdam 2026 and the slides can be found here TL; DR Whenever a feature requires voluntary adoption, standard A/B testing breaks: low adoption flattens the topline, and self-selection makes adopters incomparable to non-adopters. Combining Randomized Encouragement Design (RED) with Double Machine Learning (DoubleML) recovers two answers -- the causal lift for adopters and the rollout's overall impact. When they diverge, the gap turns an ambiguous topline into a sharp product decision -- build a better product, or build a better adoption funnel. 1. The Opt-In Barrier Across Demand and Supply Across the tech industry, many platform features rely on voluntary adoption. A customer chooses whether to claim a promotional discount. A traveller opts into a loyalty program. An e-commerce seller enables a smart-pricing tool. In every case, the platform cannot force adoption -- and the feature's true impact becomes hard to measure, on both demand and supply. At Booking.com this challenge spans both sides of the marketplace -- travellers choosing to log in, partners choosing to adopt new features. Unlike a search-ranking change that applies to 100% of traffic, opt-in features create a "trilemma" for Product Data Science: Voluntary Adoption (the "Opt-In" Barrier): Users must actively enable the feature. A standard A/B test cannot separate the product's effect from the motivation that drove users to adopt it. Extreme Heterogeneity: Travellers range from once-a-year holidaymakers to travel agencies booking thousands of nights; partners range from single-apartment hosts to hotel chains. This variance is noise on both sides. Finite Sample Sizes: Opt-in features target a finite sub-segment, so we cannot simply "run the test longer" to gain power. When these stack up, a flat topline can hide a strong product behind a weak adoption funnel. Genuinely strong features get killed, and resources flow into the wrong int