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?Learn how to
Who should attendData analysts, statisticians, and economists in the fields of finance, telecommunications, pharmaceuticals, and retail and in the public sector, who have an understanding of basic statistics and SAS programming. Those who work in areas of economics, program evaluation, and real-world effectiveness studies will find this course highly relevant.
Before attending this course, you should have completed the following 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||180 days||English||4,080 DKK|