Research & Publications
Laura Forastiere works at the intersection between statistical methodology and applied global public health research. Her methodological research is focused on methods for assessing causal inference for evidence-based research, exploring the mechanisms underlying the effect of an intervention including causal pathways through intermediate variables or mechanisms of peer influence and spillover between connected units. Her research explores modeling, inferential, and other methodological issues that often arise in applied problems with complex clustered and network data, and standard statistical theory and methods are no longer adequate to support the goals of the analysis.Her work on this topic has provided statistical methods for investigating causal mechanisms and spillover effects in clustered encouragement designs and observational network data, statistical methods for assessing the effect of an intervention on changes in the network structure, as well as experimental designs for evaluating the effectiveness of targeting strategies on networks. Another major component of her research refers to analyzing experiments affected by post-treatment variables, such as non-compliance or truncation by death, or irregular designs where the treatment is assigned according to some cutoff rule.
Behavioral Sciences; Health Plan Implementation; HIV; Global Health; Causality; Clinical Trial; Social Networking; Observational Study
Public Health Interests
Global Health; Infectious Diseases; Bayesian Statistics; Health Equity, Disparities, Social Determinants and Justice; Implementation Science
- Encouragement, experience and spillover effects in a field experiment on teens’ museum attendanceForastiere, L., Lattarulo, P., Mariani, M., Mealli, F. & Razzolini, L. “Encouragement, experience and spillover effects in a field experiment on teens’ museum attendance ”. Forthcoming in Journal of Business and Economics Statistics. https://doi.org/10.1080/07350015.2019.1647843
- Principal ignorability in mediation analysis: through and beyond sequential ignorabilityForastiere, L., Mattei, A. & Ding, P. (2018). “Principal ignorability in mediation analysis: through and beyond sequential ignorability”. Forthcoming in Biometrika . https://doi.org/10.1093/biomet/asy053 .
- Estimating causal effects on social networksForastiere, L., Mealli, F., Wu, A. & Airoldi E (2018). “Estimating causal effects on social networks”. IEEE DSAA Proceedings, 2018.
- Hierarchical graphical model for learning about functional network determinantsAliverti, E., Forastiere, L., Padellini, T., Paganin, S. & Wit, E. “Hierarchical graphical model for learning about functional network determinants”. Springer Proceedings in Mathematics & Statistics - Contributions to Neural Data Science, 2018.
- Posterior Predictive P-values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy VariablesForastiere, L., Mealli, F. & Miratrix L. (2017). “Posterior Predictive P-values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy Variables”. Bayesian Analysis , 13(3), 681-701.
- More Powerful Multiple Testing in Randomized Experiments with Non-ComplianceLee, J.J., Forastiere, L., Miratrix, L, Pillai, N.S. (2017). “More Powerful Multiple Testing in Randomized Experiments with Non-Compliance”. Statistica Sinica, 27(3), 1319-1345.
- Reduced Polynomial Classifier using Within-Class Standardizing TransformScarano, G., Forastiere, L., Colonnese, S. & Rinauro, S.. “Reduced Polynomial Classifier using Within-Class Standardizing Transform”. Proceedings of the 5th International Symposium on Communications, Control and Signal Processing, ISCCSP 2012, Rome, Italy, 2-4 May 2012
- Brain waves based user recognition using the ‘Eyes Closed Resting Conditions’ protocolCampisi, P., Scarano, G., and Babiloni, F., and DeVico Fallani, F., Colonnese, S., Maiorana, E. & Forastiere, L.. “Brain waves based user recognition using the ‘Eyes Closed Resting Conditions’ protocol”. IEEE International Workshop on Information Forensics and Security (WIFS), December, 2011
- P008 Does error feedback modality influence potential generation BCI During operation?Tocci A., Aloise F. ,Ferrez P.W., Forastiere L., Mattia D., Babiloni F., Millan J.D., Marciani M.G. & Cincotti,F. “P008 Does error feedback modality influence potential generation BCI During operation?”. Clinical Neurophysiology. vol. 119, pp. S73-S73, 2008.