Advanced Topics in Applied Empirical Methods: "Causality in Cross Sections"

All necessary informations are provided via UnivIS and OLAT. The informations on this website are out of date and will be updated when the regular teaching continues.

 

I. Syllabus:

The lecture presents strategies for estimating the effect of a binary treatment on some outcome when data are observational. For the identification of this (causal) effect, focus is on the Neyman-Rubin potential outcomes framework and, in particular, the widely-used Propensity Score (PS) techniques. Judea Pearl's causal diagrams serve as an introduction. Some auxiliary methods -like spline fitting, and regularized regression for covariate selection- are also discussed.

  1. From Randomized Experiments to Observational Data
  2. Causal Diagrams
  3. Causality Frameworks
  4. The Potential Outcomes Framework
  5. The Propensity Score I (theory and estimation)
  6. The Propensity Score II (regularization)
  7. Weighting
  8. Matching I: Exact, Nearest-Neighbor, Caliper and Radius
  9. Matching II: Kernel and Optimal Balance
  10. Stratification and Propensity Score Covariates
  11. Difference in Differences and Regression Discontinuity

 

II. Prerequisites:

  • tba

 

III. Downloads:

 

IV. Literature:

  • Imbens, G. W. & Rubin, D. B. (2015), Causal Inference for Statistics, Social, and Biomedical Science. Cambridge University Press, New York, NY.)
  • Pearl, J. (2009), Causality: Models, Reasoning and Inference. Cambridge University Press, New York, NY.
  • Morgan, S. L. & Winship, C. (2015), Counterfactuals and Causal Inference. Cambridge University Press, New York, NY.
  • Guo, S. & Fraser, M. W. (2014), Propensity Score Analysis. Sage Publications, Thousand Oaks, CA.

 

VI. Lecture:

VII. PC-Tutorial:

Access to the computer lab requires one-time registration with a Stu-Account.

The first sessions of the tutorial reinforce the intuition of causal diagrams and review the mathematics of the potential outcomes framework. In later sessions, simulations are carried out in R to explore the properties of the PS weighting, matching and stratification estimators, as well as two other estimation strategies that are popular in the econometric literature: difference in differences and regression discontinuity.

  1. Introduction to R
  2. Causal Diagrams
  3. The Potential Outcomes Framework
  4. Propensity Score Estimation
  5. Weighting)
  6. Matching
  7. Difference in Differences and Regression Discontinuity