
Policy evaluation assesses the effect of public programs on participants' socio-economic outcomes in the fields of labor, education, health, family and socio-economic policies. To retrieve causal effects, it relies on counterfactual methods that compare treated and untreated groups while accounting for confounding factors.
Policy evaluation produces rigorous evidence on what works, for whom, and under which conditions. It connects policy interventions to data, people and outcomes, asking whether a policy changes lives, how its effects arise, who experiences them, and whether results carry over to other settings.
Our work spans labour markets, skills, technological change, family policy, public finance and institutional performance. Across these areas, we keep one question in view: what changes when policy meets real institutions and inequalities across the life course? Ongoing projects include, among others, the labour-market effects of AI exposure in Luxembourg, municipal defaults and political selection in Italy, access to vocational training in Senegal, and parental leave and mothers’ wages in Luxembourg. Alongside these, a dedicated methodological programme develops new tools for causal inference.

Our aim is to estimate the causal effects of policies and interventions using rigorous research designs. Beyond average effects, we identify who gains and who loses, and how effects vary across workers, firms, population groups and institutional settings. We also seek to understand the mechanisms and channels through which policies produce their effects, to assess how these effects change over time, and to test whether they hold across specifications and contexts. Alongside this applied work, we develop and refine causal-inference methods that strengthen evaluation practice. Ultimately, we turn evidence into policy learning that informs programme design, targeting, implementation and periodic review.

Our work relies on causal designs such as difference-in-differences, natural experiments, instrumental variables and other quasi-experimental approaches. We combine these with current causal and machine-learning methods, which allow flexible estimation and the analysis of heterogeneous effects. Causal mediation analysis helps us understand how, and through which channels, policies generate their effects. Rather than relying on averages alone, we examine distributional and group-specific effects. These analyses draw on rich administrative, survey, geocoded and digital data, including matched employer–employee records and web-derived measures.




Systemic firm transformation determined by green and digital innovations: what are the key X-drivers behind this change?
SYNERGIX examines which firms adopt AI and green technology, what drives these decisions, and how adoption affects productivity, organisational structure, workforce skills and wages. It uses rich Norwegian administrative data, and the findings are relevant to Luxembourg as a similar small, high-income economy that adopts new technologies.
Michela Bia (PI), Christina Gathmann, Natella Agikyan, Terry Gregory, Martin Huber (University of Fribourg, steering committee), Georg Graetz (Uppsala University, steering committee)
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Investigating the influence of artificial intelligence on regional economies and firm dynamics
IMPACT-AI combines machine learning and natural language processing with large firm-level datasets to map how AI adoption spreads across regions and industries. It then estimates the effects on market concentration, wages and spillovers among firms, including start-ups.
Michela Bia (PI), Christina Gathmann, Mattia Laudi (PhD student)
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Causal mediation analysis and machine-learning-based estimators
CAME develops new causal-inference methods that combine causal mediation analysis with machine learning, to identify the mechanisms through which policies produce their effects. Applications include genetic instruments in the UK “Understanding Society” data and a doubly robust sample-selection correction applied to the US Job Corps training programme.
Michela Bia (PI), Martin Huber (University of Fribourg)
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Impact assessment of circular economy initiatives through integrated life cycle sustainability assessment
CIRCUSTAIN assesses the environmental, economic and social sustainability of circular economy practices, focusing on PVC in the construction sector. It also captures linked effects such as workers’ health and rebound effects from job creation.
Thomas Schaubroeck (PI), Michela Bia (PI of WP3), Antonino Marvuglia, Enrico Benetto, Arno Biwer, Bruno Domange, Thomas Gibon, Mélanie Guiton, Claudia Hitaj, Thomas Navarrete Gutiérrez, Sébastien Zinck
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Analysing the impact of artificial intelligence on employment, occupations and skills in Luxembourg
AILES examines how the diffusion of AI between 2018 and 2026 is reshaping employment, occupations and skills across economic sectors in Luxembourg, and which groups of workers are most affected. The results will provide a forward-looking diagnosis to help public policy anticipate future needs in employment, skills and training.
Christina Gathmann (PI), Andrea Albanese, Najada Feimi, Julio Garbers, Eric Guastalli, Thuc Uyen Nguyen-Thi, Lorraine Saju