Disentangling Confounding and Nonsense Associations

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Related lectures (32)

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Covers causal analysis of observational data, pitfalls, tools for valid conclusions, and addressing confounding variables.

Causal Inference: Understanding Treatment Effects

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

Causal Inference: Estimands and Ontologies

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

Causal Effects Bounds: Sensitivity Parameters on Risk Difference Scale

Explores deriving bounds for causal effects using sensitivity parameters on the risk difference scale, addressing limitations and proposing new approaches.

Front Door Criterion: Adjustment Formula

Explores the front door criterion for valid adjustment sets in causal inference.

Causal Inference: Understanding Associations and Effects

Explores the distinction between association and causation in statistical analysis, highlighting the limitations of association in inferring causation.

Causal Inference & Directed Graphs

Explores causal inference, directed graphs, and fairness in algorithms, emphasizing conditional independence and the implications of DAGs.

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.

Causal Inference in Cognitive Neuroscience

Explores the promises of causal inference in cognitive neuroscience using neurostimulation approaches to understand brain-behavior relationships.

Model Selection and Local Geometry

Explores model selection challenges in causal models and the impact of local geometry on statistical inference.