Randomized Trials and Simpson's Paradox

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Related lectures (32)

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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.

Algorithms to Live By: Optimal Stopping and Logarithmic Regret

Explores optimal stopping, explore/exploit, Bayes' Rule, and logarithmic regret in bandit solutions.

Assessing Market Regulation

Explores the assessment of market regulation, focusing on high-frequency trading and the impact of regulatory changes on liquidity and market quality.

Implementation Research: Strategies and Results

Explores implementation research strategies, outcomes, and scaling-up challenges in real-world settings.

Causality and Clinical Studies

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

Observational Studies: Pitfalls and Valid Conclusions

Highlights the pitfalls of observational studies and the importance of randomized experiments.

Epidemiological Parameters Inference from Clinical Data

Explores the challenges of inferring epidemiological parameters from clinical data, focusing on COVID-19 and the complexities of estimating infection fatality ratios.

Randomized Clinical Trial of Brewed Greenatectomy

Discusses the randomized clinical trial of brewed greenatectomy and related topics.

Causal Inference: Understanding Treatment Effects

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