Oscar A Mitnik
Biographic Data
| ID | 6938319 |
|---|---|
| NAME | Oscar A Mitnik |
| GIVEN NAMES | Oscar A |
| FAMILY NAME | Mitnik |
| SIGNATURE | MITNIK O A |
| AFFILIATIONS | University of Miami |
| ORCID | 0000-0002-7323-4559 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2019 |
| H-INDEX | 0 |
Connecting to Economic Opportunity
Comparing Treatments across Labor Markets
We study the effectiveness of nonexperimental strategies in adjusting for comparison group differences when using data from several programs, each implemented at a different location, to compare their effect if implemented at alternative locations. First, we adjust for individual characteristics differences simultaneously across all groups using unconfoundedness-based and conditional difference-in-difference methods for multiple treatments. Secon…
Dealing with limited overlap in estimation of average treatment effects
Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of overlap can lead to imprecise estimates, and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used ad hoc methods for trimming the sample. We develop a systematic approach to addressing …
Nonparametric Tests for Treatment Effect Heterogeneity
In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests…
No prominent works on this page.
Nonparametric Tests for Treatment Effect Heterogeneity
In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests…
Dealing with limited overlap in estimation of average treatment effects
Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of overlap can lead to imprecise estimates, and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used ad hoc methods for trimming the sample. We develop a systematic approach to addressing …
Comparing Treatments across Labor Markets
We study the effectiveness of nonexperimental strategies in adjusting for comparison group differences when using data from several programs, each implemented at a different location, to compare their effect if implemented at alternative locations. First, we adjust for individual characteristics differences simultaneously across all groups using unconfoundedness-based and conditional difference-in-difference methods for multiple treatments. Secon…
Connecting to Economic Opportunity
Advanced Causal Inference Techniques (3 works) · Computer Science (3 works) · Economics (3 works) · Average treatment effect (2 works) · Econometrics (2 works) · Mathematics (2 works) · Propensity score matching (2 works) · Statistical Methods in Clinical Trials (2 works) · Statistics (2 works) · Bus rapid transit (1 work)