EPSE 581C: Causal Inference for Applied Researchers
2018/19, Summer Term 1
See the full syllabus here.
Office hours:
Mondays & Wednesdays 1:30 - 2:30 PM in Scarfe 2526.
At most other days and times, I will be in my office in Scarfe 2526, so feel free to drop-by.
Meeting time and location:
Mondays & Wednesdays, 9:30 - 12:30 PM, Scarfe 204A
Course overview:
This course will cover modern techniques in causal inference. We will study the main causal models in use and their derivative statistical techniques. Criticism of these models and techniques will be heavily emphasized. Mathematical foundations will form a component of the material, though our main focus will be on practical applications for researchers in a variety of disciplines.
Download RStudio here:
RStudio Open Source License
Recommended textbooks: You can access/download these from the UBC Library
- Causal Inference, Miguel A. Hernán and James M. Robins. An excellent text that dissects the the Neyman-Rubin causal model.
- Counterfactuals and Causal Inference: Methods and Principles for Social Research, Stephen L. Morgan and Christopher Winship.
Office hours:
Mondays & Wednesdays 1:30 - 2:30 PM in Scarfe 2526.
At most other days and times, I will be in my office in Scarfe 2526, so feel free to drop-by.
Meeting time and location:
Mondays & Wednesdays, 9:30 - 12:30 PM, Scarfe 204A
Course overview:
This course will cover modern techniques in causal inference. We will study the main causal models in use and their derivative statistical techniques. Criticism of these models and techniques will be heavily emphasized. Mathematical foundations will form a component of the material, though our main focus will be on practical applications for researchers in a variety of disciplines.
Download RStudio here:
RStudio Open Source License
Recommended textbooks: You can access/download these from the UBC Library
- Causal Inference, Miguel A. Hernán and James M. Robins. An excellent text that dissects the the Neyman-Rubin causal model.
- Counterfactuals and Causal Inference: Methods and Principles for Social Research, Stephen L. Morgan and Christopher Winship.
Class Notes:
-Class 1: Intro to causal inference: Fisherian experimental design, randomization, control, manipulation
-Class 2: Confounding, Simpson's paradox
-Class 3: Quasi-experiments: Regression discontinuity designs
-Class 4: Model misspecification
-Class 5: More on model misspecification; unbiasedness and consistency of estimators
-Class 6: Even more on model misspecification; the Neyman-Rubin causal model; intro to matching
-Class 7: Matching and propensity scores
-Class 8: Practical implementation and challenges of propensity scores
-Class 9: Recap of propensity scores; intro to instrumental variables
-Class 10: Instrumental variables
-Class 11: Instrumental variables; miscellany; course recap
-Class 1: Intro to causal inference: Fisherian experimental design, randomization, control, manipulation
-Class 2: Confounding, Simpson's paradox
- Case Study: Busta et al. (2017)
- Case Study: Durante et al. (2013)
-Class 3: Quasi-experiments: Regression discontinuity designs
- Case Study: Ludwig and Miller (2007)
-Class 4: Model misspecification
-Class 5: More on model misspecification; unbiasedness and consistency of estimators
-Class 6: Even more on model misspecification; the Neyman-Rubin causal model; intro to matching
-Class 7: Matching and propensity scores
-Class 8: Practical implementation and challenges of propensity scores
- Case Study: Rubin (1995)
-Class 9: Recap of propensity scores; intro to instrumental variables
- Case Study: Connors et al. (1996)
-Class 10: Instrumental variables
- Case Study: Angrist & Krueger (2001)
- Case Study: Leigh & Schembri (2004)
-Class 11: Instrumental variables; miscellany; course recap
- Case Study: Draca et al. (2009)
Homeworks:
-HW1 problems (due May 27th)
-HW2 problems (due June 19th)
-HW3 problems (due June 30th)
-HW1 problems (due May 27th)
-HW2 problems (due June 19th)
-HW3 problems (due June 30th)
- R code for HW3