University · Statistics · Applied Statistical Modeling

Causal Inference and Propensity Scores

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The potential outcomes framework, the identifying assumptions required for causal claims from observational data, and propensity score methods (matching, weighting, stratification) for estimating treatment effects.

Inhaltsübersicht

  • The Potential Outcomes Framework and Why Correlation Is Not Causation
  • The Propensity Score and Its Role in Confounding Adjustment
  • Assessing Covariate Balance and Overlap
  • Related Designs and the Limits of Observational Causal Claims
Diagram illustrating the potential outcomes framework showing treated and control counterfactual outcomes
Pixabay – Pixabay License

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Learn Causal Inference and Propensity Scores — Applied Statistical Modeling Statistics | Summary, Flashcards & Quiz