Arbeitspapier
Bounding treatment effects by pooling limited information across observations
We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as sample averages over functions of the observed outcomes such that the contribution of each outcome only depends on the treatment status of a limited number of observations. No information pooling across observations leads to so-called "Manski bounds", while unlimited information pooling leads to standard inverse propensity score weighting. We explore the intermediate range between these two extremes and provide corresponding inference methods. We show in Monte Carlo experiments and through an empirical application that our bounds are indeed robust and informative in practice.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP43/21
Unconfoundedness
Limited Overlap
Partial Identification
Weidner, Martin
- DOI
-
doi:10.47004/wp.cem.2021.4321
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:22 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Lee, Sokbae
- Weidner, Martin
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2021
Ähnliche Objekte (12)
Identification of a competing risks model with unknown transformations of latent failure times
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating panel data duration models with censored data
Endogeneity quantile regression models: A control function approach
Nonparametric tests of conditional treatment effects
Characterization of the asymptotic distribution of semiparametric M-estimators
Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited
Uniform confidence bands for functions estimated nonparametrically with instrumental variables
Uniform confidence bands for functions estimated nonparametrically with instrumental variables
Sparse quantile regression
Semiparametric estimation of a panel data proportional Hazards model with fixed effects
Identification of a competing risks model with unknown transformations of latent failure times
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating panel data duration models with censored data
Endogeneity quantile regression models: A control function approach
Nonparametric tests of conditional treatment effects
Characterization of the asymptotic distribution of semiparametric M-estimators
Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited
Uniform confidence bands for functions estimated nonparametrically with instrumental variables
Uniform confidence bands for functions estimated nonparametrically with instrumental variables
Sparse quantile regression
Semiparametric estimation of a panel data proportional Hazards model with fixed effects
Identification of a competing risks model with unknown transformations of latent failure times
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating potential outcome distributions using local instrumental variables with an application to changes in college enrollment and wage inequality
Estimating panel data duration models with censored data
Endogeneity quantile regression models: A control function approach
Nonparametric tests of conditional treatment effects
Characterization of the asymptotic distribution of semiparametric M-estimators
Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited
Uniform confidence bands for functions estimated nonparametrically with instrumental variables
Uniform confidence bands for functions estimated nonparametrically with instrumental variables
Sparse quantile regression