Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2022
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914139316682752 |
|---|---|
| author | Rambachan, Ashesh Coston, Amanda Kennedy, Edward |
| author_facet | Rambachan, Ashesh Coston, Amanda Kennedy, Edward |
| contents | Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding -- when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected units with the same observed characteristics. These bounds formalize common empirical strategies including proxy outcomes and instrumental variables. Our estimators work across bounding strategies and performance measures such as conditional likelihoods, mean square error, and true/false positive rates. Using administrative data from a large Australian financial institution, we show that varying confounding assumptions substantially affects credit risk predictions and fairness evaluations across income groups. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_09844 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding Rambachan, Ashesh Coston, Amanda Kennedy, Edward Econometrics Computers and Society Machine Learning Methodology Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding -- when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected units with the same observed characteristics. These bounds formalize common empirical strategies including proxy outcomes and instrumental variables. Our estimators work across bounding strategies and performance measures such as conditional likelihoods, mean square error, and true/false positive rates. Using administrative data from a large Australian financial institution, we show that varying confounding assumptions substantially affects credit risk predictions and fairness evaluations across income groups. |
| title | Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding |
| topic | Econometrics Computers and Society Machine Learning Methodology |
| url | https://arxiv.org/abs/2212.09844 |