Cross-validated Targeted Maximum Likelihood Estimation (CV-TMLE)
Alan Hubbard is a Professor of Biostatistics at UC Berkeley. His research focuses on the application of statistics to population studies with particular expertise in semi-parametric models and the use of machine learning in causal inference, as well as applications in high dimensional biology.

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Highly Adaptive Lasso (HAL) in Causal Inference

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Targeted Learning: Towards a future informed by real world evidence

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Avicii, Dua Lipa, Coldplay, Martin Garrix & Kygo, The Chainsmokers Style - SUMMER DEEP HOUSE Mix

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Linear mixed effects models
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Power BI Dataflows Tutorial and Best Practices [Full Course] 📊

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2. An Introduction toTargeted Maximum Likelihood Estimation of Causal Effects

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Practical Considerations for Specifying a Super Learner
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Power Automate Beginner to Pro Tutorial [Full Course]

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The Riskiest Moment of the AI Bubble

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Normal Distributions Explained – With Real-World Examples

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JavaScript Tutorial For Beginners | JavaScript Training | JavaScript Course | Intellipaat

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Targeted Machine Learning in Action in the ICU

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Allen Bradley PLC Programming Sequencer Tutorial. Sequence Control

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How To Think SO Clearly People Assume You're Brilliant

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Introduction to Causal Inference and Treatment Effects in Stata
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Your Phone Is Destroying Your Sense of Meaning | Arthur Brooks [ARC 2026]

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Covariate adjustment in randomized studies with time-to-event endpoints

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Free Event: Power BI Beginner to Pro 2026 Edition - Full Hands-On Tutorial

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1. Targeted Machine Learning for Causal Inference based on Real World Data

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