Published Sep 6, 2022

SDS 607: Inferring Causality — with Jennifer Hill

Jon Krohn and Jennifer Hill explore the art and science of inferring causality in data, delving into Bayesian statistics, hierarchical models, and innovative tools like Bayesian Additive Regression Trees to uncover deeper insights in data science.

Episode Highlights

Topics covered

Episode Highlights

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SDS 607: Inferring Causality — with Jennifer Hill

Topics covered

Popular Clips

Causal Inference Challenges

Hierarchical Modeling Insights

Causal Tools Explained

Research and Real-World Impact

Language and Causality

Causal Inference Insights

Bayesian Statistics Insights

Bayesian Modeling Insights

Benefits of BART

Nutritional Confusion

Social Media Balance

Causal Inference in Genetics

Understanding Causal Questions

Multilevel Modeling Insights

Episode Highlights

Causal Inference

Bayesian Statistics

Hierarchical Models

Related Episodes

SDS 617: Causal Modeling and Sequence Data — with Sean Taylor

SDS 613: Causal Machine Learning — with Emre Kiciman

SDS 506: Supervised vs Unsupervised Learning — with Jon Krohn

SDS 581: Bayesian, Frequentist, and Fiducial Statistics in Data Science — with Xiao-Li Meng

SDS 507: Bayesian Statistics — with Rob Trangucci

SDS 539: Interpretable Machine Learning — with Serg Masís

SDS 595: Data Engineering 101 — with Joe Reis and Matt Housley

SDS 587: Data Engineering for Data Scientists — with Mark Freeman

SDS 479: Knowledge Graphs — with Maureen Teyssier

SDS 487: Fixing Dirty Data — with Susan Walsh

SDS 558: @JonKrohnLearns's Answers to Questions on Machine Learning

SDS 504: Classification vs Regression — with Jon Krohn

SDS 435: Scaling Up Machine Learning — with Erica Greene

838: Consciousness and Machines — with Jennifer K. Hill

SDS 437: Data Science at a World-Leading Hedge Fund — with Claudia Perlich

SDS 607: Inferring Causality — with Jennifer Hill

Topics covered

Popular Clips

Episode Highlights

Causal InferenceJennifer Hill, a Professor of Applied Statistics at NYU, discusses the complexities of inferring causality in data science. She covers the challenges, teaching strategies, and tools for making causal inferences.

Causal Inference

Bayesian StatisticsJennifer Hill explains the foundational concepts of Bayesian statistics and its practical applications in causal inference. She highlights the benefits of using Bayesian Additive Regression Trees (BART) for making accurate causal inferences.

Bayesian Statistics

Hierarchical ModelsJennifer Hill discusses the intricacies of hierarchical modeling and its practical applications. She highlights the advantages of these models in making more granular inferences in various fields.

Hierarchical Models

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