Estimating the Value of Evidence-Based Decision Making
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910015723405312 |
|---|---|
| author | Abadie, Alberto Agarwal, Anish Imbens, Guido Jia, Siwei McQueen, James Stepaniants, Serguei Torres, Santiago |
| author_facet | Abadie, Alberto Agarwal, Anish Imbens, Guido Jia, Siwei McQueen, James Stepaniants, Serguei Torres, Santiago |
| contents | In an era of data abundance, statistical evidence is increasingly critical for business and policy decisions. Yet, organizations lack empirical tools to assess the value of evidence-based decision making (EBDM), optimize statistical precision, and balance the costs of evidence-gathering strategies against their benefits. To tackle these challenges, this article introduces an empirical framework to estimate the value of EBDM and evaluate the return on investment in statistical precision and project ideation. The framework leverages parametric and nonparametric empirical Bayes methods to account for parameter heterogeneity and measure how statistical precision changes the value of evidence. The value extracted from statistical evidence depends critically on how organizations translate evidence into policy decisions. Commonly used decision rules based on statistical significance can leave substantial value unrealized and, in some cases, generate negative expected value. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13681 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Estimating the Value of Evidence-Based Decision Making Abadie, Alberto Agarwal, Anish Imbens, Guido Jia, Siwei McQueen, James Stepaniants, Serguei Torres, Santiago Methodology Machine Learning Econometrics In an era of data abundance, statistical evidence is increasingly critical for business and policy decisions. Yet, organizations lack empirical tools to assess the value of evidence-based decision making (EBDM), optimize statistical precision, and balance the costs of evidence-gathering strategies against their benefits. To tackle these challenges, this article introduces an empirical framework to estimate the value of EBDM and evaluate the return on investment in statistical precision and project ideation. The framework leverages parametric and nonparametric empirical Bayes methods to account for parameter heterogeneity and measure how statistical precision changes the value of evidence. The value extracted from statistical evidence depends critically on how organizations translate evidence into policy decisions. Commonly used decision rules based on statistical significance can leave substantial value unrealized and, in some cases, generate negative expected value. |
| title | Estimating the Value of Evidence-Based Decision Making |
| topic | Methodology Machine Learning Econometrics |
| url | https://arxiv.org/abs/2306.13681 |