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Related lectures (31)

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Front Door Criterion: Adjustment Formula

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

Disentangling Confounding and Nonsense Associations

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

Causal Inference & Directed Graphs

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

Model Selection and Local Geometry

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

Causal Inference: Understanding Treatment Effects

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

System Composition and Properties

Discusses LTI system composition, causality, stability, and output determination.

Causal Inference: Adjustment Sets

Explains adjustment sets in causal inference, emphasizing valid and candidate sets.

Optimization and Simulation: Introduction to Simulation

Introduces state-of-the-art methods in optimization and simulation, covering topics like statistical analysis, variance reduction, and simulation projects.

Causal Inference: Understanding Associations and Effects

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

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.