# causal-machine-learning Published articles for causal-machine-learning. 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]() Original publisher: [Read original article]() Author: Lin Jia Published: 2026-05-22T08:05:43Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [A/B Testing](), [doubleml](), [Data Science](), [Tool]() Tags: [a-b-testing](), [causal-inference](), [causal-machine-learning](), [causality](), [data-science](), [doubleml](), [e-commerce](), [experimentation](), [featured](), [measuring]() ### 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 ## Scaling Subscriptions at The New York Times with Real-Time Causal Machine Learning DevFeed: [Scaling Subscriptions at The New York Times with Real-Time Causal Machine Learning]() Original publisher: [Read original article]() Author: Rohit Supekar Published: 2025-10-03T15:19:24Z Content type: article Language: en Sources: [New York Times]() Topics: [Machine Learning & Artificial Intelligence](), [Algorithms](), [Optimization](), [real-time]() Tags: [account](), [algorithms](), [causal-inference](), [causal-machine-learning](), [data](), [data-science](), [machine-learning](), [optimization](), [users]() ### AI overview The New York Times describes replacing its Dynamic Meter machine-learning model with real-time algorithms that decide within milliseconds whether users receive access. The system uses causal machine learning and multi-objective optimization to balance subscription, registration, engagement, conversion, and business constraints across its subscription funnel. ### Source excerpt How real-time algorithms and causal ML transformed our digital subscription funnel from static paywalls to dynamic, millisecond decision-makingIllustration by Mathieu Labrecque The New York Times became a subscription-first news and lifestyle service with the launch of its paywall in 2011. Since then, our subscription strategy has evolved substantially. Initially, users could access a limited number of free articles per month before they encountered the paywall. In 2019, we began personalizing this number using a Machine Learning (ML) model -- The Dynamic Meter. In the past few years, we have replaced this model with real-time algorithms that decide, typically within milliseconds, whether to grant access. These algorithms are tailored to balance and optimize the tradeoff between several business Key Performance Indicators (KPIs), while also allowing us the flexibility to adjust for any business constraints. This article further details the motivation behind these algorithms and their design based upon principles from causal machine learning and multi-objective optimization. Our subscription funnel The New York Times has a tiered subscription funnel (Figure 1), consisting of unregistered, registered, and subscribed users. This funnel is designed to provide non-subscribers with limited access to our content, allowing them to discover our offerings. At other times, the content may be blocked by a digital "wall". We have two types of walls -- a registration wall that asks a user to register for a free account or log in, and a paywall that asks a user to subscribe. A large number of users are unregistered -- they may be shown either a registration wall or a paywall. Once a user is in the registered state, they can be shown only a paywall. Figure 1: The New York Times subscription funnelOptimizing the subscription funnel Optimizing who sees the registration wall or the paywall -- and when -- is a very relevant question for our business. While blocking access encourages users t