Michela Bia


  • 11, Porte des Sciences, Maison des Sciences Humaines

    L-4366 Esch-sur-Alzette/Belval



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Personal profile

Research summary

Michela Bia is a Research Fellow in the field of “Program Evaluation” at the Luxembourg Institute of Socio-Economic Research, LISER.

In 2006, she has been Visiting Student Researcher at the Department of Agricultural & Resource Economics of the University of California in Berkeley. She received her PhD in Applied Statistics from the University of Florence in 2007 and worked from 2007-2009 as Researcher at Laboratorio Revelli of Collegio Carlo Alberto in Turin. She has been teaching statistics and econometrics at the University of Turin, Milan, University of Piemonte Orientale “Amedeo Avogadro”, Pompeu Fabra, Potsdam University and machine learning based methods for Big Data at the University of Luxembourg. 

In 2010 she received a Post-Doc Position at LISER supported by the Fond National de la Recherché (FNR) of Luxembourg, to develop semiparametric and nonparametric techniques for the estimation of direct, indirect causal effects in policy evaluation. She also won a European Social Fund (ESF) project for the 'Evaluation of Active Labour Market Policies in Luxembourg', co-financed by the Ministry of Labour, Employment and the Social and Solidarity Economy of Luxembourg and LISER during the period of October 2015-January 2018 (amount: €485,000).  She was involved in the 'Childcare Project' financed by the FNR between January 2014 and February 2018, for the definition of optimal policies in the market for childcare using data from Luxembourg. She is currently collaborating in the 'Parent Project', for the evaluation of parental leave policies in Luxembourg. In 2020, she got the 'Causal Mediation Analysis and Machine Learning based Estimators' project accepted for funding under the framework of the Inter Mobility Programme supported and sponsored by the FNR.

Her main fields of interest are: causal Inference, program evaluation, nonparametric methods, machine learning, high-dimensional data, labour market, mendelian randomization.

Her works have been published on The Stata Journal, Statistical Methods and Applications, Advances in Latent Variables, Applied Economics Letters, BMJ, Journal of the Royal Statistical Society: Series A, Journal of Business and Economic Statistics. The Stata softwares developed in 2008 and 2014 are downloadable from the Boston College Department of Economics (Statistical Software Components).


Education/Academic qualification


External positions

Affiliate Professor, Social Sciences, University of Luxembourg

Free keywords

  • Causal Analysis
  • Policy Evaluation
  • Machine Learning
  • High Dimensional Data
  • Mendelian Randomization

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