printer friendly pageEDUC 260A: Applications of Causal Inference Methods (EPI 239, STATS 209B)
See
http://rogosateaching.com/stat209/. Application of potential outcomes formulation for causal inference to research settings including: mediation, compliance adjustments, time-1 time-2 designs, encouragement designs, heterogeneous treatment effects, aggregated data, instrumental variables, analysis of covariance regression adjustments, and implementations of matching methods. Prerequisite: an introduction to causal inference methods such as
STATS209.
Terms: Win
| Units: 2
Instructors:
Rogosa, D. (PI)
;
Fager, D. (TA)
EPI 239: Applications of Causal Inference Methods (EDUC 260A, STATS 209B)
See
http://rogosateaching.com/stat209/. Application of potential outcomes formulation for causal inference to research settings including: mediation, compliance adjustments, time-1 time-2 designs, encouragement designs, heterogeneous treatment effects, aggregated data, instrumental variables, analysis of covariance regression adjustments, and implementations of matching methods. Prerequisite: an introduction to causal inference methods such as
STATS209.
Terms: Win
| Units: 2
Instructors:
Rogosa, D. (PI)
;
Fager, D. (TA)
STATS 209: Introduction to Causal Inference
This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. Topics include potential outcomes, randomization, observational studies, matching, covariate adjustment, AIPW, heterogeneous treatment effects, instrumental variables, regression discontinuity, and synthetic controls. Prerequisites: basic probability and statistics, familiarity with R.
Terms: Aut
| Units: 3
STATS 209B: Applications of Causal Inference Methods (EDUC 260A, EPI 239)
See
http://rogosateaching.com/stat209/. Application of potential outcomes formulation for causal inference to research settings including: mediation, compliance adjustments, time-1 time-2 designs, encouragement designs, heterogeneous treatment effects, aggregated data, instrumental variables, analysis of covariance regression adjustments, and implementations of matching methods. Prerequisite: an introduction to causal inference methods such as
STATS209.
Terms: Win
| Units: 2
Instructors:
Rogosa, D. (PI)
;
Fager, D. (TA)
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