Causal Inference: Understanding Treatment Effects

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Related lectures (32)

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Causal Analysis of Observational Data

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

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 Inference & Directed Graphs

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

Disentangling Confounding and Nonsense Associations

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

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

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.

Model Selection and Local Geometry

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

Simpson's Paradox: Understanding and Randomized Trials

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