Confounding

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Related lectures (12)

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Disentangling Confounding and Nonsense Associations

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

Simpson's Paradox: Understanding and Randomized Trials

Explores Simpson's Paradox with a real-world example and emphasizes the significance of randomized trials.

Causal Analysis of Observational Data

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

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.

Causal Inference: Understanding Treatment Effects

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

Causality and Clinical Studies

Explores causality, clinical studies, new drug evaluation, confounding factors, and data analysis methods.

Experimental Design: Strategies and Analysis

Discusses experimental design strategies, factors, responses, treatment combinations, and degrees of freedom.

Randomized Trials and Simpson's Paradox

Covers randomized clinical trials, confounding variables, ethical challenges, and Simpson's paradox in data analysis.

Experimental Design in Biostatistics

Introduces experimental design in biostatistics, covering research process, hypothesis testing, ANOVA modeling, and interpretation of results.

Front Door Criterion: Adjustment Formula

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