Causal inference

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Related lectures (32)

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Model Selection and Local Geometry

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

Demand Forecasting: Methods & Cognitive Biases

Explores demand forecasting methods and cognitive biases affecting decision-making processes.

Causal Inference: Understanding Treatment Effects

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

Mendelian Randomization: Insights and Challenges

By Qingyuan Zhao delves into Mendelian randomization, exploring its history, challenges, and recent methods to handle pleiotropy.

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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 Reasoning in Healthcare: ML Guidelines & Dataset Shifts

Explores causal reasoning in healthcare AI, ML guidelines, dataset shifts, bias impact, and multimodal learning.

Causality and Invariant Machine Learning

Explores causality, correlation, and spurious correlations in machine learning, emphasizing bias mitigation and invariance across environments.

Causal Inference: Adjustment Sets

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

Causal Inference: Learning Graph Structures

Explores causal inference through learning graph structures for causal reasoning from observational data.