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Propensity Score Matching, Adjustment, and Randomized Experiments

Course Details
Tuition (USD): $1,650.00 • Classroom (2 days)
Course Details
GSA (USD): $1,496.22 • Classroom (2 days)

This course focuses on testing whether the results of a program can be attributed to a given cause. For example, was the increase in customer sales due to mailing of sales flyers? Was the health improvement due to the new medication? What conclusion can be drawn? The following cases are examined: randomized controlled experiments and observational studies that require adjustment to reduce bias by using propensity score analysis through either propensity score matching or propensity score adjustment.

Skills Gained

  • identify situations in which the simple method of multiple linear regression is inadequate
  • apply quasi-experimental analysis methods to real-world data for the following techniques: Propensity Score Matching and Propensity Score Adjustment.

Who Can Benefit

  • Data analysts or statisticians, in the fields of finance, telecommunications, pharmaceuticals, retail, and the public sector, who have an understanding of basic statistics and SAS programming


  • Before attending this course, you should complete or have the equivalent working experience of the following courses:
  • SAS(R) Programming I: Essentials
  • Statistics I: Introduction to ANOVA, Regression, and Logistic Regression

Course Details

Introduction to Causation

  • introduction
  • program evaluation
  • introduction to causation
  • evaluation designs (optional)
  • checklist for evaluations (optional)

Randomized Experiments

  • introduction
  • randomized experiments
  • multiple linear regression
  • issues with randomized design
  • endogeneity

Propensity Score Matching: Theory and Practice

  • introduction
  • real-world non-random treatment assignment
  • propensity score matching basics
  • interpreting the propensity matched results

Propensity Score Adjustment: Theory and Practice

  • introduction
  • examples of real-world propensity score adjustment
  • motivation behind propensity score adjustment
  • real-world practice using banking example

Group Discussion of Real-World Examples

  • introduction
  • discuss examples of real-world quasi-experimental designs
  • real-world data from students
  • review of key steps in discussed methods
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