The Center for Predictive and Causal Methods (CPCM) is an interdisciplinary research group dedicated to advancing empirical methods for research, policy, and practice in education, health, labor markets, and social policy. CPCM combines methodological innovation, rigorous data analysis, and interdisciplinary collaboration to strengthen the quality and use of empirical evidence.
CPCM builds on the work of the Amsterdam Center for Learning Analytics (ACLA), established in 2016 as a research group focused on improving outcomes through high-quality data, methodological development, and collaboration across disciplines. While ACLA originally centered on learning analytics, CPCM broadens this foundation into a wider methodological program connecting causal inference, predictive modelling, intervention and policy evaluation, and data-informed decision-making.
Our ambition is to advance the role of empirical evidence in research, policy, and practice. We study how empirical approaches can be used to identify what works, for whom, and under what conditions, and how data can support the early identification of risks, needs, and opportunities. By integrating causal and predictive perspectives, CPCM aims to contribute to more credible evidence, better-targeted interventions, and more adaptive practices across education, health, labor markets, and social policy.
CPCM pursues this ambition through methodological innovation, transparent evidence generation, and interdisciplinary collaboration. We develop and evaluate methods, apply them to real-world research and policy questions, train students and researchers, and collaborate with academic, professional, and societal partners. Our vision is a research environment in which empirical methods support better understanding, informed decision-making, effective interventions, and improved societal outcomes.
STAFF MEMBERS
- Prof. dr. Chris van Klaveren, Full Professor
- Dr. Ilja Cornelisz, Associate Professor