Introduction to Bayesian Additive Regression Trees (BART) for Causal Inference
Dr. Nicole Bohme Carnegie, Assistant Professor of Statistics in the Department of Mathematical Sciences at Montana State University, provides an introduction to BART machine learning for causal inference. BART has been a leading estimation method in the Atlantic Causal Inference Data Challenges since its inauguration in 2016.
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Causal Inference of Longitudinal Exposures, presented by Dr. Mireille Schnitzer
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Bayesian Additive Regression Trees: A Practitioners Guide with George Perrett - nyhackr Oct Meetup
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2. An Introduction toTargeted Maximum Likelihood Estimation of Causal Effects
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A Tutorial on Conformal Prediction
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Andrew Gelman - Bayesian Methods in Causal Inference and Decision Making
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2. Bayesian Optimization
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#80 Bayesian Additive Regression Trees (BARTs), with Sameer Deshpande
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Avicii, Dua Lipa, Coldplay, Martin Garrix & Kygo, The Chainsmokers Style - SUMMER DEEP HOUSE Mix
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1. Targeted Machine Learning for Causal Inference based on Real World Data
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Andrew Gelman: Introduction to Bayesian Data Analysis and Stan with Andrew Gelman
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Bayes theorem, the geometry of changing beliefs
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Causal Inference in Python: Theory to Practice
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An introduction to Causal Inference with Python – making accurate estimates of cause and effect from
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Supervised Learning and Linear Discriminants
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Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019
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Foundations of causal inference and its impacts on machine learning webinar
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What is causal inference, and why should data scientists know? by Ludvig Hult
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Practical Considerations for Specifying a Super Learner
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Benjamin Vincent - What-if- Causal reasoning meets Bayesian Inference | PyData Global 2022
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