Causal AI
causaLens’ NeurIPS 2020 paper sets out a framework for benchmarking causal discovery techniques time series data. causaLens researchers Andrew Lawrence, Marcus Kaiser, Rui Sampaio, and Maksim Sipos introduce a novel framework for evaluating and benchmarking causal discovery methods for time-series data. The paper – which also evaluates prominent causal discovery algorithms, and sets out how…
Founded in 1945, this non-profit organization was born from an innovative healthcare program for shipyard workers during World War II. Today, it’s one of the largest managed care organizations in the United States, serving over 12 million members. What sets Kaiser Permanente apart is its revolutionary model that combines health insurance, hospitals, and medical groups…
Prediction is at the core of running an efficient supply chain. Whether it’s forecasting demand or the weather; prediction enables our vastly complex global supply chains to operate in real-time and deliver on customer needs. However, the current approach to machine learning relies on past patterns and correlations to make predictions about the future –…
Causal approaches empower data scientists to answer questions that cannot be answered using standard machine learning techniques, leading to a clearer connection to ROI from their models. Examples of such questions include: ● “What is the optimal treatment to change a specific outcome?”● “What is the effect of intervening on a certain input parameter?”● “What…
AT&T, a titan in the telecommunications industry, has long been at the forefront of technological innovation. With millions of customers and an ever-expanding network, AT&T faces unique data challenges that require cutting-edge solutions. In recent years, the company has turned to advanced data science and artificial intelligence to tackle these challenges, with a particular focus…
In the fast-paced world of retail, success has always boiled down to three fundamental goals: get people to buy, encourage them to return, and entice them to spend more on each visit. Simple in theory, yet increasingly complex in practice. As we navigate the data-rich landscape of the 21st century, retailers find themselves at a…
At a glance Traditional portfolio optimization theories fail in the real world, because they are based on misleading correlations. Current machine learning approaches suffer from the same problem. Causal AI outperforms all other approaches in terms of risk-adjusted returns, and it is intuitive, transparent and explainable. Leading asset managers are benefiting from intelligent portfolio optimization with Causal AI. Challenges in portfolio…
A leading Mobile App company sees a projected reduction of 5% in annual marketing spend using decisionOS Customer A large Mobile App Company Industry Consumer Apps Use Case Marketing Mix Modeling Value 15x ROI, 5% reduction in marketing spend The Challenge Understanding and quantifying which marketing channels drive installations and recurring paying customers is key…
TL;DR Double Machine Learning can be used to learn unbiased estimates of causal effects decisionOS by causaLens offers the only package that allows learning unbiased structural causal models using Double Machine Learning, providing accurate answers to interventional (‘what-ifs’) and counterfactual (‘what-would-have’) questions causaLens has spent many hours of R&D time implementing a model-agnostic Full Graph Double…
Introduction How are Causal AI models different from Bayesian networks?The two types of models have some superficial similarities, but they also have significant differences. Bayesian networks (BNs) simply describe patterns of correlations between variables. Causal AI models capture the underlying processes that drive those statistical relationships. This paradigm shift makes Causal AI models more flexible,…