Marie-Andree Somers
Biographic Data
| ID | 5458871 |
|---|---|
| NAME | Marie-Andree Somers |
| GIVEN NAMES | Marie-Andree |
| FAMILY NAME | Somers |
| SIGNATURE | SOMERS M |
| AFFILIATIONS | MDRC, New York, NY |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 8 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2019 |
| H-INDEX | 2 |
A Framework for Learning From Null Results
A substantial number of randomized trials of educational interventions that have been conducted over the past two decades have produced null results, with either no impact or an unreliable estimate of impact on student achievement or other outcomes of interest. The investment of time and money spent implementing such trials warrants more useful information than simply “this didn’t work.” In this article, we propose a framework for defining null r…
Principal Stratification
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 coun…
A Framework for Learning From Null Results
A substantial number of randomized trials of educational interventions that have been conducted over the past two decades have produced null results, with either no impact or an unreliable estimate of impact on student achievement or other outcomes of interest. The investment of time and money spent implementing such trials warrants more useful information than simply “this didn’t work.” In this article, we propose a framework for defining null r…
Principal Stratification
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 coun…
Principal Stratification
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 coun…
A Framework for Learning From Null Results
A substantial number of randomized trials of educational interventions that have been conducted over the past two decades have produced null results, with either no impact or an unreliable estimate of impact on student achievement or other outcomes of interest. The investment of time and money spent implementing such trials warrants more useful information than simply “this didn’t work.” In this article, we propose a framework for defining null r…
Advanced Causal Inference Techniques (2 works) · Computer Science (2 works) · Econometrics (2 works) · Mathematics (2 works) · Psychology (2 works) · Behavioral and Psychological Studies (1 work) · Causal inference (1 work) · Context (archaeology (1 work) · Counterfactual thinking (1 work) · Data mining (1 work)