Principal Stratification
A Tool for Understanding Variation in Program Effects Across Endogenous Subgroups
Datos Bibliográficos
| ID | 12470973 |
|---|---|
| Autores | Lindsay C Page (0000-0001-5932-6791, University of Pittsburgh, Pittsburgh, PA, USA, autor de correspondencia), Avi Feller (0000-0001-7319-5468, University of California, Berkeley, CA, USA), Todd Grindal (0009-0005-8461-5508, Abt Associates, Cambridge, MA, USA), Luke W Miratrix (0000-0002-0078-1906, Harvard University Press), Luke Miratrix (Harvard University, Cambridge, MA, USA), Marie-Andree Somers (MDRC, Los Angeles, CA, USA), Marie‐Andrée Somers (0000-0002-8079-2305, Manpower Demonstration Research Corporation) |
| Año | 2015 |
| Volumen | 36 |
| Número | 4 |
| Páginas | 514-531 |
| Fecha de publicación | 2015-07-28 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | American Journal of Evaluation (JOURNAL) |
| Identificadores de la revista | ISSN: 1098-2140 • E-ISSN: 1557-0878 |
| Editorial | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/1098214015594419 |
| OpenAlex | W2252647298 |
| Idioma | EN |
| Citas recibidas | 3 |
| Referencias citadas | 47 |
Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous subgroups, or principal strata, based on post-randomization behaviors under both the observed and the counterfactual experimental conditions. These principal strata give structure to such research questions and provide a framework for determining estimation strategies to obtain desired effect estimates. This article provides a nontechnical primer to principal stratification. We review selected applications to highlight the breadth of substantive questions and methodological issues that this method can inform. We then discuss its relationship to instrumental variables analysis to address binary noncompliance in an experimental context and highlight how the framework can be generalized to handle more complex posttreatment patterns. We emphasize the counterfactual logic fundamental to principal stratification and the key assumptions that render analytic challenges more tractable. We briefly discuss technical aspects of estimation procedures, providing a short guide for interested readers
Causal inference · Context (archaeology · Counterfactual thinking · Data science · Econometrics · Economics · Geography · Management science · Principal (computer security · Advanced Causal Inference Techniques · Computer Science · Mathematics · Psychology · Social Psychology · Statistical Methods and Bayesian Inference · Statistical Methods in Clinical Trials
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On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
Semiparametric Difference-in-Differences Estimators
Bayesian Inference for Causal Effects
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Identification of Causal Effects Using Instrumental Variables
Commentary
On Analysis of Symmetrically Predicted Endogenous Subgroups
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| Obras citantes distintas | 3 |
|---|---|
| Citas por año | 0,33 |
| Intervalo de citas | 2017 - 2024 (8) |
| Velocidad de citación | recent |
| Altamente citado | No |
| Tipos de cita | Neutras: 3 |