Lectures related to Causal Inference: Adjustment Sets

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

Explores causality, correlation, and spurious correlations in machine learning, emphasizing bias mitigation and invariance across environments.

Covers the concept of difference equations and properties of causal vs. stable systems.

Covers block diagrams and causal LTI systems with rational transfer functions.

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Investigates how birth month influences athlete success, analyzing Japanese athletes' dataset to explore trends in birthdates and occupations.