Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.19693 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913052013625344 |
|---|---|
| author | Centorrino, Samuele Parmeter, Christopher F. |
| author_facet | Centorrino, Samuele Parmeter, Christopher F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19693 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Causal Analysis of the Stochastic Frontier Model Centorrino, Samuele Parmeter, Christopher F. Econometrics 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. |
| title | Recent Advances in Causal Analysis of the Stochastic Frontier Model |
| topic | Econometrics |
| url | https://arxiv.org/abs/2604.19693 |