Open AccessPosted Content
A causal analysis of mother’s education on birth inequalities
Reads0
Chats0
TLDR
In this article, a causal analysis of the mother's educational level on the health status of the newborn, in terms of gestational weeks and weight, is presented, based on a finite mixture structural equation model, the parameters of which have a causal interpretation.Abstract:
We propose a causal analysis of the mother’s educational level on the health status of the newborn, in terms of gestational weeks and weight. The analysis is based on a finite mixture structural equation model, the parameters of which have a causal interpretation. The model is applied to a dataset of almost ten thausand deliveries collected in an Italian region. The analysis confirms that standard regression overestimates the impact of education on the child health. With respect to the current economic literature, our findings indicate that only high education has positive consequences on child health, implying that policy efforts in education should have benefits for welfare.read more
Citations
More filters
Proceedings Article
What is Causal Inference
Abstract: This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (Neyman-Rubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) the answers to three types of causal queries: (1) queries about the effect of potential interventions, (2) queries about counterfactuals, and (3) queries about the direct (or indirect) effect of one event on another.
Journal ArticleDOI
Structural Equation Models in the Social Sciences.
Posted Content
STEMM: A General Finite Mixture Structural Equation Model
TL;DR: The statistical theory, simulation evidence on the performance of the EM estimation algorithm, and apply the model to a psychological study on the role of emotion in goal-directed behavior are described.
Latent Variable Modeling in Heterogeneous Populations
TL;DR: MIMIC structural modeling is shown to be a useful method for detecting and describing heterogeneity that cannot be handled in regular multiple-group analysis, and random effects models connect with emerging methodology for multilevel structural equation modeling of hierarchical data.
References
More filters
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Categorical Data Analysis
TL;DR: In this article, categorical data analysis was used for categorical classification of categorical categorical datasets.Categorical Data Analysis, categorical Data analysis, CDA, CPDA, CDSA
Journal ArticleDOI
Estimating causal effects of treatments in randomized and nonrandomized studies.
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
Book
Finite Mixture Models
Geoffrey J. McLachlan,David Peel +1 more
TL;DR: The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the mathematical and statistical literature.
Journal ArticleDOI
Causality: Models, Reasoning and Inference
Related Papers (5)
Mother's education and child development: Evidence from the compulsory school reform in China
Maternal Time, Child Care and Child Cognitive Development: The Case of Single Mothers
Raquel Bernal,Michael Keane +1 more