Recent Advances in Causal Analysis of the Stochastic Frontier Model
Samuele Centorrino, Christopher F. Parmeter
TLDR
This paper reviews recent advances in integrating causal inference methods with stochastic frontier models to analyze productivity and efficiency.
Key contributions
- Reviews nascent literature on integrating causal inference with stochastic frontier models.
- Discusses modeling approaches and empirical issues relevant for applied researchers.
- Demonstrates how the stochastic frontier model can be adapted for causal analysis.
- Identifies challenges and summarizes core findings in this interdisciplinary area.
Why it matters
Causal inference is vital in social sciences but has lagged in productivity studies using stochastic frontier models. This review bridges that gap, making these models more accessible for robust causal analysis of efficiency. It offers a roadmap for researchers.
Original Abstract
Causal inference methods (instrumental variables, difference-in-differences, regression discontinuity, etc.) are primary tools used across many social science milieus. One area where their application has lagged however, is in the study of productivity and efficiency. A main reason for this is that the nature of the stochastic frontier model does not immediately lend itself to a causal framework when interest hinges on an error component of the model. This paper reviews the nascent literature on attempts to merge the stochastic frontier literature with causal inference methods. We discuss modeling approaches and empirical issues that are likely to be relevant for applied researchers in this area. This review shows how this model can be easily put within the confines of causal analysis, reviews existing work that has already made inroads in this area, addresses challenges that have yet to be met and discusses core findings.
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