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A causal analysis of mother’s education on birth inequalities

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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.

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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.
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References
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Finite Mixture Models

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.
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