I specialize in causal inference and experimentation for decisions with real business impact. My focus is on problems where standard analytics breaks down: interference, temporal dependence, noisy outcomes, and environments where small effects can still matter commercially. I design experiments that help decision-makers distinguish signal from noise and make informed decisions. At ADC, I have led work on pricing and commercial optimization, including designing experiments, measuring causal impact, and informing decisions in complex operational settings. Much of this work involves generating credible evidence where clean textbook assumptions do not hold. I hold a PhD in econometrics and have published in the 𝘑𝘰𝘶𝘳𝘯𝘢𝘭 𝘰𝘧 𝘵𝘩𝘦 𝘈𝘮𝘦𝘳𝘪𝘤𝘢𝘯 𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘢𝘭 𝘈𝘴𝘴𝘰𝘤𝘪𝘢𝘵𝘪𝘰𝘯 and the 𝘑𝘰𝘶𝘳𝘯𝘢𝘭 𝘰𝘧 𝘌𝘤𝘰𝘯𝘰𝘮𝘦𝘵𝘳𝘪𝘤𝘴. I am also the founder of Time Series Lab, and I write regularly about time series, causal inference, and experimentation. You can find an overview of all posts here: timeserieslab.com/linkedin-series My main interests are experimentation, causal inference, pricing, and decision-making under complexity.