Contributions to the theory and practice of latent variable modelling and causal inference

Unobservable concepts are frequent in economics: utility, expectations, beliefs, competitiveness of firms, productivity of a worker can all be viewed as latent variables. Although not directly observable, each of these concepts can be indirectly measured by some related observable indicators. The first two chapters of this thesis provide a deeper understanding of the tools available to empirical researchers to measure latent variables. In the first chapter, we use factor analysis to measure decent work which was originally conceptualized by the International Labour Organization. In the second chapter, we investigate bias correction methods when factor scores are used as regressors. Focusing on nonlinear regression including covariates, we propose two bias-corrected estimators. Finally, in the third chapter, we show the detrimental effect of small amounts of contaminated data on the estimated causal impact of treatment and propose robust version of standard causal inference estimators