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This book attempts to sum up reviews and discussions about causal inference from the statistical and econometric literatures, describing and comparing these two main approaches. Particular attention is on dynamic models that allow to control for unobserved heterogeneity when longitudinal data are available. The application concern the labour market where in the last years, the average length of employment relationships has significantly been reduced with a consequent higher rate of total turnover. In order to define the characteristics of a good job a new definition of stable job is considered, linked to the duration and not to the type of contract. Furthermore, the coherence with the university studies associated to that work experience is considered. Having at disposal administrative panel data on both Lombardy labour market and records of the graduates of three biggest University of Milan, a dynamic model to study the impact of the first stable job coherent with the university studies on the future job coherence is performed. Given the categorical nature of the variable of interest, that is the coherence, a dynamic logit causal model has been performed.

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Isabella Romeo is Post-doc research Fellow at the Department of Statistics and Quantitative Methods, University of Milano-Bicocca (Italy).Her areas of research include topics such as longitudinal analysis, causal inference in a counterfactual framework, education and labour market applications.

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