Causal Inference under Selection on Observables in Operations Management Research: Matching Methods and Synthetic Controls

43 Pages Posted: 27 Dec 2022 Last revised: 4 Jun 2024

See all articles by Ovunc Yilmaz

Ovunc Yilmaz

Leeds School of Business, University of Colorado Boulder

Yoonseock Son

University of Notre Dame - Mendoza College of Business - IT, Analytics, and Operations Department

Guangzhi Shang

Florida State University - College of Business

Hayri Alper Arslan

University of Texas at San Antonio - College of Business - Department of Economics

Date Written: June 01, 2024

Abstract

The majority of recent empirical papers in Operations Management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as observational data lacks the benefit of random treatment assignment, estimating causal effects poses challenges. In the specific scenario where one can reasonably assume that all confounding factors are observed - referred to as selection on observables - matching methods and synthetic controls can assist researchers to replicate a randomized experiment, the most desirable setting for drawing causal inferences. In this paper, we first present an overview of matching methods and their utilization in the OM literature. Subsequently, we establish the framework and provide pragmatic guidance for propensity score matching and coarsened exact matching, which have garnered considerable attention in recent OM studies. Following this, we conduct a comprehensive simulation study that compares diverse matching algorithms across various scenarios, providing practical insights derived from our findings. Finally, we discuss synthetic controls, a method that offers unique advantages over matching techniques in specific scenarios and is expected to become even more popular in the OM field in the near future. We hope that this paper will serve as a catalyst for promoting a more rigorous application of matching and synthetic control methodologies.

Keywords: Selection on observables, propensity score matching, coarsened exact matching, synthetic controls, method review

Suggested Citation

Yilmaz, Ovunc and Son, Yoonseock and Shang, Guangzhi and Arslan, Hayri Alper, Causal Inference under Selection on Observables in Operations Management Research: Matching Methods and Synthetic Controls (June 01, 2024). Available at SSRN: https://ssrn.com/abstract=4310241 or http://dx.doi.org/10.2139/ssrn.4310241

Ovunc Yilmaz (Contact Author)

Leeds School of Business, University of Colorado Boulder ( email )

Boulder, CO 80309-0419
United States

Yoonseock Son

University of Notre Dame - Mendoza College of Business - IT, Analytics, and Operations Department ( email )

Notre Dame, IN 46556
United States

Guangzhi Shang

Florida State University - College of Business ( email )

423 Rovetta Business Building
Tallahassee, FL 32306-1110
United States

Hayri Alper Arslan

University of Texas at San Antonio - College of Business - Department of Economics ( email )

6900 North Loop 1604 West
San Antonio, TX 78249
United States

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