Establishing Causal Inferences: Propensity Score Matching, Heckman's Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models
Business Knowledge Series course
Presented by Howard S. Friedman, Ph.D., Professor, Columbia University, and Partner, DataMed Solutions LLC
This course introduces some methods commonly used in program evaluation and real-world effectiveness studies, including two-stage modeling, interrupted time series, regression discontinuity, and propensity score matching. These methods help address questions such as: Which medicine is more effective in the real world? Did an advertising program have an impact on sales? More generally, are the changes in outcomes causally related to the program being run?
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Who should attend
Before attending this course, you should have completed the SAS Programming 1: Essentials and Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression courses or have the equivalent working experience.
Prior study of multiple linear regression modeling is required.
This course addresses Base SAS, SAS/ETS, SAS/STAT software.
Heckman's Two-Stage Model
|Title||Duration||Access Period||Language||Fee||Add to Cart|
|Establishing Causal Inferences: Propensity Score Matching, Heckman's Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models||14.0 hours||365 days||English||990 USD / 2.0 EPTO|