Sharp Bounds on Functionals of the Joint Distribution in the Analysis of Treatment Effects
Bibliographic Data
| ID | 19418389 |
|---|---|
| Authors | Thomas M Russell (University of Toronto, corresponding author) |
| Year | 2021 |
| Volume | 39 |
| Issue | 2 |
| Pages | 532-546 |
| Publication date | 2021-04-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2019.1684300 |
| OpenAlex | W2982419922 |
| Language | EN |
| Citations received | 1 |
| References cited | 34 |
This article proposes an identification and estimation method that allows researchers to bound continuous functionals of the joint distribution of potential outcomes from the literature on treatment effects. The focus is on a model where no restrictions are imposed on treatment selection. The method can sharply bound interesting parameters when analytical bounds are difficult to derive, can be used in settings in which instruments are available, and can easily accommodate additional model constraints. However, computational considerations for the method are found to be important and are discussed in detail.
Econometrics · Joint probability distribution · Machine learning · Mathematical optimization · Statistics · Upper and lower bounds · Advanced Causal Inference Techniques · Computer Science · Engineering · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Statistical Methods and Inference · Applied Mathematics
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| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |