Member Training: Coarsened Exact Matching, an Alternative to Propensity Score Matching

The objective for quasi-experimental designs is to establish cause and effect relationships between the dependent and independent variables. However, they have one big challenge in achieving this objective: lack of an established control group.

There are ways, though, to create a post-hoc control group. One way is to match non-treated subjects with treated subjects.

The most common matching method is Propensity Score Matching. Gaining popularity as a matching method is Coarsened Exact Matching. How are these matching methods different?

To understand the differences, this Stats Amore Training explores the following:

  1. A discussion on why and when to match data
  2. How propensity score matching is constructed
  3. How coarsened exact matching is constructed
  4. Advantages and disadvantages of both approaches

Note: This training is a benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series.

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About the Instructor

Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.

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