Alessandra Mattei - Profile on Academia.edu (original) (raw)
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Papers by Alessandra Mattei
Missing Data and Imputation Methods
With Applications Using R, 2011
Advances in Theoretical and Applied Statistics, 2013
A Bayesian approach to causal inference in the presence of noncompliance to assigned randomized t... more A Bayesian approach to causal inference in the presence of noncompliance to assigned randomized treatment is considered. It exploits multivariate outcomes for improving estimation of weakly identified models. We maintain the monotonicity of compliance assumption, while relaxing the usually invoked exclusion restriction assumption for never-takers. Using artificial data sets, we analyze the properties of the posterior distribution of causal estimands to evaluate the potential gains of jointly modelling more than one outcome. The approach can be used to assess robustness with respect to deviations from structural identifying assumptions. It can also be extended to the analysis of observational studies with instrumental variables where exclusion restriction assumptions are usually questionable.
Discussion
Biometrics, Jan 13, 2015
Evaluating Direct and Indirect Effects in a Non-Experimental Setting Using Principal Stratification. An Application
Augmented Designs to Assess Principal Strata Causal Effects
How education affects fertility in the presence of time-varying frailty component
Bayesian inference for causal mechanisms with application to a randomized study for postoperative pain control
Bayesian Inference for Sequential Treatments under Latent Sequential Ignorability
We investigate the relationship between living standards and fertility, using a three-wave panel ... more We investigate the relationship between living standards and fertility, using a three-wave panel dataset from Indonesia to provide information on women's fertility histories and the levels of consumption expenditure in the households to which they belong. We adopt a Bayesian approach to estimation and exploit the dynamically recursive structure implied by gestation lags to identify causal effects of living standards on fertility and vice versa.
Principal Stratification (PS) is a principled framework for addressing noncompliance issues. Due ... more Principal Stratification (PS) is a principled framework for addressing noncompliance issues. Due to the latent nature of principal strata, model-based PS analysis usually involves weakly identified models and identification of causal effects relies on untestable structural assumptions, such as exclusion restriction. This article develops a Bayesian approach to exploit multivariate outcomes to sharpen inferences for weakly identified models within PS. Simulation studies are performed to illustrate the potential gains in identifiability of jointly modelling more than one outcome. The method is applied to evaluate the causal effect of a job search program on depression.
Identification of Local Causal Effects with Missing Outcome Values and an Instrument for Non Response
ABSTRACT Even in randomized experiments the identification of causal effects is often threatened ... more ABSTRACT Even in randomized experiments the identification of causal effects is often threatened by the presence of missing outcome values, with missingness possibly being non ignorable. We provide sufficient conditions under which the availability of a binary instrument for non response allows us to non parametrically point identify average causal effects in some latent subgroups of units, named Principal Strata, defined by their non response behavior in all possible combinations of treatment and instrument. Examples are provided as possible scenarios where our assumptions may be plausible.
Evaluating the Effect of University Grants on Student Dropout: Evidence from a Regression Discontinuity Design Using Bayesian Principal Stratification Analysis
The relationship between education and fertility in the presence of a time varying frailty component
The effects of a dropout prevention program on secondary students’ outcomes
A Stata package for the application of semiparametric estimators of dose–response functions
Nonparametric estimators of dose-response functions
We propose two semiparametric estimators of the dose-response function based on spline techniques... more We propose two semiparametric estimators of the dose-response function based on spline techniques. Under uncounfoundedness, the generalized propensity score can be used to estimate dose-response functions (DRF) and marginal treatment effect functions. In many observational studies treatment may not be binary or categorical. In such cases, one may be interested in estimating the dose-response function in a setting with a continuous treatment. We evaluate the performance of the proposed estimators using Monte Carlo simulation methods. The simulation results suggested that the estimated DRF is robust to the specific semiparametric estimator used, while the parametric estimates of the DRF were sensitive to model mis-specification. We apply our approach to the problem of evaluating the effect on innovation sales of Research and Development (R&D) financial aids received by Luxembourgish firms in 2004 and 2005.
Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score
ABSTRACT
A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score
In this article, we briefly review the role of the propensity score in estimating dose-response fu... more In this article, we briefly review the role of the propensity score in estimating dose-response functions as described in Hirano and Imbens (2004, Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 73-84). Then we present a set of Stata programs that estimate the propensity score in a setting with a continuous treatment, test the balancing property of the generalized
Assessing the effect of a teaching program on breast self-examination in a randomized trial with noncompliance and missing data
Missing Data and Imputation Methods
With Applications Using R, 2011
Advances in Theoretical and Applied Statistics, 2013
A Bayesian approach to causal inference in the presence of noncompliance to assigned randomized t... more A Bayesian approach to causal inference in the presence of noncompliance to assigned randomized treatment is considered. It exploits multivariate outcomes for improving estimation of weakly identified models. We maintain the monotonicity of compliance assumption, while relaxing the usually invoked exclusion restriction assumption for never-takers. Using artificial data sets, we analyze the properties of the posterior distribution of causal estimands to evaluate the potential gains of jointly modelling more than one outcome. The approach can be used to assess robustness with respect to deviations from structural identifying assumptions. It can also be extended to the analysis of observational studies with instrumental variables where exclusion restriction assumptions are usually questionable.
Discussion
Biometrics, Jan 13, 2015
Evaluating Direct and Indirect Effects in a Non-Experimental Setting Using Principal Stratification. An Application
Augmented Designs to Assess Principal Strata Causal Effects
How education affects fertility in the presence of time-varying frailty component
Bayesian inference for causal mechanisms with application to a randomized study for postoperative pain control
Bayesian Inference for Sequential Treatments under Latent Sequential Ignorability
We investigate the relationship between living standards and fertility, using a three-wave panel ... more We investigate the relationship between living standards and fertility, using a three-wave panel dataset from Indonesia to provide information on women's fertility histories and the levels of consumption expenditure in the households to which they belong. We adopt a Bayesian approach to estimation and exploit the dynamically recursive structure implied by gestation lags to identify causal effects of living standards on fertility and vice versa.
Principal Stratification (PS) is a principled framework for addressing noncompliance issues. Due ... more Principal Stratification (PS) is a principled framework for addressing noncompliance issues. Due to the latent nature of principal strata, model-based PS analysis usually involves weakly identified models and identification of causal effects relies on untestable structural assumptions, such as exclusion restriction. This article develops a Bayesian approach to exploit multivariate outcomes to sharpen inferences for weakly identified models within PS. Simulation studies are performed to illustrate the potential gains in identifiability of jointly modelling more than one outcome. The method is applied to evaluate the causal effect of a job search program on depression.
Identification of Local Causal Effects with Missing Outcome Values and an Instrument for Non Response
ABSTRACT Even in randomized experiments the identification of causal effects is often threatened ... more ABSTRACT Even in randomized experiments the identification of causal effects is often threatened by the presence of missing outcome values, with missingness possibly being non ignorable. We provide sufficient conditions under which the availability of a binary instrument for non response allows us to non parametrically point identify average causal effects in some latent subgroups of units, named Principal Strata, defined by their non response behavior in all possible combinations of treatment and instrument. Examples are provided as possible scenarios where our assumptions may be plausible.
Evaluating the Effect of University Grants on Student Dropout: Evidence from a Regression Discontinuity Design Using Bayesian Principal Stratification Analysis
The relationship between education and fertility in the presence of a time varying frailty component
The effects of a dropout prevention program on secondary students’ outcomes
A Stata package for the application of semiparametric estimators of dose–response functions
Nonparametric estimators of dose-response functions
We propose two semiparametric estimators of the dose-response function based on spline techniques... more We propose two semiparametric estimators of the dose-response function based on spline techniques. Under uncounfoundedness, the generalized propensity score can be used to estimate dose-response functions (DRF) and marginal treatment effect functions. In many observational studies treatment may not be binary or categorical. In such cases, one may be interested in estimating the dose-response function in a setting with a continuous treatment. We evaluate the performance of the proposed estimators using Monte Carlo simulation methods. The simulation results suggested that the estimated DRF is robust to the specific semiparametric estimator used, while the parametric estimates of the DRF were sensitive to model mis-specification. We apply our approach to the problem of evaluating the effect on innovation sales of Research and Development (R&D) financial aids received by Luxembourgish firms in 2004 and 2005.
Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score
ABSTRACT
A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score
In this article, we briefly review the role of the propensity score in estimating dose-response fu... more In this article, we briefly review the role of the propensity score in estimating dose-response functions as described in Hirano and Imbens (2004, Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 73-84). Then we present a set of Stata programs that estimate the propensity score in a setting with a continuous treatment, test the balancing property of the generalized
Assessing the effect of a teaching program on breast self-examination in a randomized trial with noncompliance and missing data