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INFORMATION FOR

Laura Forastiere, PhD

Assistant Professor of Biostatistics (Biostatistics); Affiliated Faculty, Yale Institute for Global Health

Contact Information

Laura Forastiere, PhD

Research Summary

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.

Coauthors

Research Interests

Behavioral Sciences; Health Plan Implementation; HIV; Global Health; Causality; Clinical Trial; Biostatistics; Social Networking; Observational Study

Public Health Interests

Global Health; Infectious Diseases; Bayesian Statistics; Health Equity, Disparities, Social Determinants and Justice; Implementation Science

Selected Publications

  • Identification and Estimation of Treatment and Interference Effects in Observational Studies on NetworksForastiere L, Airoldi E, Mealli F. Identification and Estimation of Treatment and Interference Effects in Observational Studies on Networks Journal Of The American Statistical Association 2020, 116: 901-918. DOI: 10.1080/01621459.2020.1768100.
  • Estimating short term impact of fine airborne particles on mortality using a semiparametric Generalized Propensity Score approachM B, L F, M C. Estimating short term impact of fine airborne particles on mortality using a semiparametric Generalized Propensity Score approach Environmental Epidemiology 2019, 3: 18-19. DOI: 10.1097/01.ee9.0000605812.71544.67.
  • Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum AttendanceForastiere L, Lattarulo P, Mariani M, Mealli F, Razzolini L. Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum Attendance Journal Of Business And Economic Statistics 2019, 39: 244-258. DOI: 10.1080/07350015.2019.1647843.
  • P018 Pay-it-forward gonorrhea and chlamydia testing among chinese men who have sex with men: a cluster randomized controlled trialZhang T, Yang F, Tang W, Huang W, Wang Y, Alexander M, Forastiere L, Kumar N, Li K, Zou F, Yang L, Mi G, Lee A, Zhu W, Vickerman P, Wu D, Yang B, Christakis N, Tucker J. P018 Pay-it-forward gonorrhea and chlamydia testing among chinese men who have sex with men: a cluster randomized controlled trial Sexually Transmitted Infections 2019, 95: a89. DOI: 10.1136/sextrans-2019-sti.227.
  • 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
  • Hierarchical Graphical Model for Learning Functional Network DeterminantsAliverti E, Forastiere L, Padellini T, Paganin S, Wit E. Hierarchical Graphical Model for Learning Functional Network Determinants 2018, 257: 23-36. DOI: 10.1007/978-3-030-00039-4_2.
  • Principal ignorability in mediation analysis: through and beyond sequential ignorabilityForastiere L, Mattei A, Ding P. Principal ignorability in mediation analysis: through and beyond sequential ignorability Biometrika 2018, 105: 979-986. DOI: 10.1093/biomet/asy053.
  • Estimating Causal Effects on Social NetworksForastiere L, Mealli F, Wu A, Airoldi E. Estimating Causal Effects on Social Networks 2018, 00: 60-69. DOI: 10.1109/dsaa.2018.00016.
  • Posterior Predictive $p$-Values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy MeasuresForastiere L, Mealli F, Miratrix L. Posterior Predictive $p$-Values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy Measures Bayesian Analysis 2018, 13 DOI: 10.1214/17-ba1062.
  • 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.
  • More Powerful Multiple Testing in Randomized Experiments with Non-ComplianceLee J, Forastiere, L, Miratrix L, Pillai N. More Powerful Multiple Testing in Randomized Experiments with Non-Compliance Statistica Sinica 2017 DOI: 10.5705/ss.202016.0116.
  • Reduced Polynomial Classifier using Within-Class Standardizing TransformScarano G, Forastiere L, Colonnese S, Rinauro S. Reduced Polynomial Classifier using Within-Class Standardizing Transform 2012, 1-4. DOI: 10.1109/isccsp.2012.6217825.
  • 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, Babiloni F, De Vico Fallani F, Colonnese S, Maiorana E, Forastiere L. Brain waves based user recognition using the “Eyes Closed Resting Conditions” protocol 2011, 1-6. DOI: 10.1109/wifs.2011.6123138.
  • 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 feedback modality influence error potential generation during BCI operation?Tocci A, Aloise F, Ferrez P, Forastiere L, Mattia D, Babiloni F, del R. Millàn J, Marciani M, Cincotti F. P008 Does feedback modality influence error potential generation during BCI operation? Clinical Neurophysiology 2008, 119: s73. DOI: 10.1016/s1388-2457(08)60279-5.
  • 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.