Category: Stream 1
Spotify: Unlocking Business Impact with Causal System Design
In this talk, we introduce a systematic approach to AI-driven business solutions by integrating causal reasoning throughout the system design process. Drawing on Aristotle’s Four Causes, NASA’s Technology Readiness Levels (TRLs), and Judea Pearl’s causal modeling, we present a powerful framework for Causal System Design. Through a real-world hospital demand forecasting case, we’ll demonstrate how understanding the root causes of business challenges—beyond data and predictions—unleashes the full potential of AI. Attendees will discover how causal insights enhance decision-making, streamline operations, and align AI systems with business goals, driving transformative impact and lasting value.
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Dr Athanasios Vlontzos Research Scientist, Spotify
Nubank: Continuous Treatments: Challenges from the Banking Industry
In this presentation, I’ll explore the central role of Causal Inference in credit allocation and optimization within the banking business. I’ll go over how determining the amount of credit credit and setting the right interest rate can be framed as a Conditional Average Treatment Effect estimation problem. Since both credit and price are continuous treatments, I’ll talk about the main challenges that come with them and some exciting attempts at solving those problems.
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Matheus Facure Staff Data Scientist, Nubank
Booking.com: Converted data is enough for most promotion uplift modeling problems in E-commerce
Promotions are crucial in e-commerce for increasing user engagement and building customer loyalty. These incentives naturally raise causal questions, as they function as interventions that alter the business’s status quo. Effectively managing the cost-effectiveness of promotions is crucial for creating campaigns that benefit both customers and businesses. This involves addressing a dual causal inference challenge: balancing the uplift in rewards, such as conversion and revenue, against the corresponding increase in costs. In e-commerce, promotional costs and rewards are typically incurred only upon conversion, resulting in zero cost and reward for non-conversions. Given that conversion rates are often below 10%, the data is predominantly zero-inflated, with over 90% of the observations providing limited information about the targets of interest. In this presentation, we demonstrate that in scenarios with triggered costs and rewards, effective promotion modeling can be achieved by applying uplift modeling techniques exclusively to converted data from randomized experiments (e.g., A/B tests). This approach enables the accurate calculation of key metrics, such as lift, relative metrics (revenue, utility, etc.), and Return on Investment (ROI), while also reducing training times by more than 90% without compromising model performance.
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Hugo Proença Senior Machine Learning Scientist, Booking.com
Zalando: Causal ML in Practice: Estimating Uplift with Selection into Treatment
While experimentation is the golden standard for causal inference and is widely adopted in the industry, it is sometimes infeasible or undesirable. In these settings, a common causal estimate is the incremental impact of a feature or program that is released to the whole customer base, but only a subset of users adopts it or subscribes to it. In this talk, we present some practical learnings from the fashion industry, with an application on the incrementality of subscription programs.
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Matteo Courthoud Senior Applied Scientist, Zalando
causaLens: Accelerating Data Science Productivity with AI Agents
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Darko Matovski Founding CEO, causaLens
Bergfreunde: “Decoding eCommerce” – Why Causal AI adds unique value to managing business performance at Bergfreunde.de
Be inspired about the business rationale of how Europe’s largest Outdoor eCommerce Retailer Bergfreunde.de (known in the UK as www.alpinetrek.co.uk/) uses Causal AI to intervene on key operational business metrics and forecast each week’s sales and profit and learn how to decode eCommerce operations algorithmically
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Christopher Barth Head of Data & Analytics, Bergfreunde
Croud: How to be better at marketing with causal AI
At Croud, we are taking media planning and optimization to the next level by combining causal AI with Media Mix Modeling (MMM). This integration not only enhances our models’ ability to explain and analyze marketing outcomes but also makes them more robust and insightful. By bringing together the best of both approaches, we are able to offer our clients deeper insights and more effective strategies. In this presentation, we will dive into how this blend of techniques is transforming the way we approach media planning.
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Fernanda Hinze Data Scientist, Croud
BP: Causal AI in BP
Causal AI is already used in the energy sector, but it’s still key for advancing our operations. We use it to quickly turn data into insights and then into actions, which is crucial for managing large operations. We’re looking at how Causal AI is currently used in different parts of the energy industry and how it might be used in the future. We also consider how today’s causal methods are setting the stage for new technologies, including a look at an advanced wind farm concept.
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Cristiano Da Cruz Machine Learning Researcher, BP
Spotify: Counterfactual reasoning and what it’s good for
Causal reasoning is vital for effective reasoning in many domains, from healthcare to economics. In medical diagnosis, for example, a doctor aims to explain a patient’s symptoms by determining the diseases causing them. This is because causal relations, unlike correlations, allow one to reason about the consequences of possible treatments and to answer counterfactual queries. In this talk I will present some recent work done with my collaborators about how one can learn and reason with counterfactual distributions, and why this is importantly for decision making. In all cases I will strive to motivate and contextualise the results with real word examples.
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Ciarán M. Gilligan-Lee Head of Causal Inference Lab & Senior Research Manager, Spotify
M&S: Markdown Optimization with Causal ML
The dynamic retail environment presents unique challenges for causal inference in optimal pricing. Evolving business tactics, seasonality, changing product assortments, and censored demand create highly contextual, heterogeneous, and time-varying price response effects. In this presentation, we will demonstrate how causal ML is utilized for markdown optimization at Marks & Spencer. We will explain our approach to predicting price elasticities, accounting for uncertainty, and address the pitfalls of using aggregated data for causal inference. Our experiences illustrate how these methods can lead to more robust and effective decision-making processes in complex retail environments.
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Mindaugas Zickus Lead Data Scientist, Marks and Spencer
McCann Worldgroup: Causal modelling for social causes
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Gideon Wilkins Group Head of Research, McCann Worldgroup
Aleksander Molak: Causality Upside Down
When we think about causal modeling, we often focus on (critically important) assumptions necessary to make valid causal inferences. The value of this perspective is hard to overestimate. At the same time, the true goal of applied causal inference is to improve our decision-making rather than just meet causal assumptions. In this talk, we will take a look at a set of methods that allow us to make causal inferences under violated assumptions. Although some, if not all, of these methods might not lead to inferences as precise as we’d like, they can help us solve our real problem – making better decisions.
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Aleksander Molak Causal ML Tutor / Causal Bandits Host, University of Oxford / Causalpython.io