Causality and Clinical Studies

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Related lectures (24)

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Causal Inference in Epidemiology: Pregnancy and Prostate Cancer

Explores causal inference in epidemiology, focusing on COVID-19's impact on preterm birth and refining treatment strategies for prostate cancer.

Graphical Analysis of Full Factorials

Explores the graphical analysis of full factorial experiments, emphasizing efficient data analysis and interpretation of main effects and interactions.

Causal Inference: Estimands and Ontologies

Explores causal inference, emphasizing the importance of committing to an ontology for drawing causal inferences and selecting appropriate estimands.

Probability and Statistics

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Pressure: Forces and Motion

Explores pressure, forces, and motion, emphasizing their interplay and effects on surfaces.

Disentangling Confounding and Nonsense Associations

Explores statistical dependence, confounding, and causal inference methods, emphasizing the distinction between existing and new approaches.

Causal Inference: Understanding Treatment Effects

Explores treatment effects, confounding variables, and challenges in interpreting observational study results.

Data Visualization: Principles and Practices

Emphasizes the importance of data visualization techniques and practices for effective data analysis and communication.

Invariance, Causality, and Robustness

Explores invariance, causality, and robustness in data analysis, addressing challenges and implications for distribution generalization.

Peptide Therapeutics

Explores the development and advantages of peptide therapeutics, including conversion into peptidomimetics and solid phase peptide synthesis.