President Elect
One candidate will be selected.
Fabrizia Mealli
Fabrizia Mealli is Professor of Economics and Statistics at the Department of Economics of the European University Institute; she is on leave from the University of Florence where she has been Professor of Statistics since 2005 and was the founder and director of the Florence Center for Data Science. She held visiting positions at the Harvard Statistics and Biostatistics Departments in 2001, 2015, and 2017.
Her research focuses on statistical methods for causal inference in experimental and observational settings, estimation techniques, simulation methods, missing data, and Bayesian inference, with applications to the social and biomedical sciences.
She is an Elected Fellow of the American Statistical Association (ASA) and sits on the Steering Committee of the European Causal Inference Meeting (EUROCIM) and of the Online Causal Inference Seminars (OCIS). She is an Associate Editor for Biometrika, the Journal of the American Statistical Association T&M, The Annals of Applied Statistics, and Observational Studies. Since 2001, Fabrizia has been teaching Causal Inference in International Schools and in Master and PhD programs around the world.
As a passionate advocate for advancing causal inference methodologies and applications, I am eager to contribute with my experience and dedication to propel the Society to new heights.
Over the years, I have witnessed the growing role of causal inference in shaping rigorous research, policy and decision-making across diverse fields.
If elected as President, I commit to enhancing the visibility and impact of the Society by showcasing the impact of causal inference in addressing real-world challenges.
I will commit to i) encouraging collaborations, exchanging ideas, and forming interdisciplinary partnerships; ii) strengthening partnerships with international organizations, and fostering a global network; iii) promoting educational activities, workshops, webinars, and educational resources to ensure members are at the forefront of causal inference developments; iv) ensuring diversity and inclusion so that all voices are heard and valued.
I am excited about the prospect of leading the Society for Causal Inference through a period of growth and innovation, having a lasting impact on research, policy, and practice.
Jason Roy
Jason Roy is Professor of Biostatistics and Chair of the Department of Biostatistics and Epidemiology at Rutgers School of Public Health. He is a Fellow of the American Statistical Association (ASA) and received the Causality in Statistics Education Award from the ASA in 2021. He has had leadership experience in statistical societies, including recently as Chair of the Council of Sections Governing Board of the ASA. He is co-author of the book “Bayesian Nonparametrics for Causal Inference and Missing Data” published in 2023.
I am excited about the opportunity to potentially serve as President of the Society for Causal Inference. Drawing upon my experience as the Founding Co-Director of the Center for Causal Inference at UPenn, I am eager to bring my passion for community building and program development to the SCI. As a relatively new Society, there are great opportunities for increasing its impact, value, and visibility. I would work with the leadership team and members of the society to explore new initiatives in educational training, mentoring, and meetings to foster research collaborations, as well as to identify creative ways to fund these endeavors. It has been great to see the causal inference community grow over the years, and I’d welcome the opportunity lead the SCI into the future!
Treasurer
One candidate will be selected.
Razieh Nabi
I am a Rollins Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University. I graduated from the Johns Hopkins University in 2021 where I had the privilege of working with Ilya Shpitser.
Many problems in the empirical sciences and rational decision making require causal, rather than associative, reasoning. The field of causal inference is concerned with establishing and quantifying cause-effect relationships to inform interventions, even in the absence of direct experimentation or randomization. Drawing valid causal conclusions from data is impeded by various factors such as the presence of unmeasured confounders, curse of dimensionality, missing and censored values, measurement error, social contagion, network interference, and data that reflect historical patterns of discrimination and inequality. The focus of my research is the development of novel causal methodologies to address these pressing challenges. My research draws on methodological insights from both machine learning/artificial intelligence, especially using graphical models, and statistical theory, especially semiparametric statistics. My applications of interest include healthcare, social justice, and public policy.Dear Members of the Society for Causal Inference,
I am honored to present my candidacy for the Treasurer position on the SCI Board. My journey in causal inference began at the start of my PhD studies under the mentorship of the esteemed Dr. Ilya Shpitser. He recommended that I attend the ACIC conference at UNC in Chapel Hill
during my first year; an experience that was transformative. It offered not just a broader introduction to the field but also a profound realization of its impact and the community it fosters. This experience
cemented my passion and set the course of my career. Witnessing the growth of our community, with the Society for Causal Inference playing a pivotal role, has deeply inspired me. I am now eager to contribute to our collective progress and to help shape the future of this vibrant field.As a prospective member of the SCI Board, my vision is clear: to nurture the growth of our society and to expand the influence of causal inference. I am dedicated to fostering an environment that is inclusive and innovative, where members at every level feel valued and motivated to contribute. Our society has been instrumental in advancing causal inference, and I am committed to maintaining its status as a leading force. In the role of Treasurer, my priority will be to ensure the financial health and sustainability of SCI. I aim to develop comprehensive and forward-looking budgets to support our ambitious initiatives, with a focus on fiscal responsibility, transparency, and strategic investment in our society’s future.
The lessons from my inaugural ACIC conference have remained with me – the excitement of discovery, the warmth of our community, and the potential to effect real change. These are the qualities I intend to
bring to the Board. If elected, I pledge to dedicate my full energy, insights, and experience to overcome the challenges and seize the opportunities that lie ahead.
I am thrilled at the prospect of serving on the SCI Board and am deeply grateful for your consideration. I look forward to the opportunity to contribute to our society’s continued success and growth. As Rachel
Platten eloquently puts it:
Like a small boat, on the ocean.
Sending big waves, into motion.
Like how a single word, can make a heart open.
I might only have one match, but I can make an explosion.And TOGETHER, we can continue push the boundaries of what is possible in causal inference, making a lasting impact on the world around us.
Warmest regards,
Razieh Nabi (she/her/hers)
Samuel Pimental
Sam Pimentel is an Assistant Professor in the Statistics Department at UC Berkeley.. His research aims at understanding causal relationships using large administrative datasets from the medical and social sciences, with a particular focus on optimal design of comparison groups and sensitivity analysis for unobserved confounding. He completed his PhD at the University of Pennsylvania in 2017, and his work has been supported by the Food and Drug Administration and by an NSF CAREER award.
I am honored to be nominated for the treasurer position on the SCI board. The SCI community has played a major role in shaping my career and inspiring my current research. I have been an SCI member since 2021 and have participated in ACIC regularly since 2014, serving on the organizing committee in 2015 and 2022 and presenting short courses or serving as a poster judge several times. As treasurer I would manage SCI funds carefully with the goal of maximizing our support for graduate students and early-career researchers through travel awards and targeted events and meetings. I would also work to ensure the long-term financial health and stability of SCI as we seek to grow our membership and our impact.
Secretary
One candidate will be selected.
Jessica Young
I am an Associate Professor in the Department of Population Medicine at the Harvard Medical School and Harvard Pilgrim Health Care Institute. I also hold a secondary faculty appointment at the Harvard Chan School of Public Health. I am an Associate Editor for Lifetime Data Analysis and Biometrics and a member of the Editorial Board of Epidemiology. I was first introduced to causal inference as a PhD student in Biostatistics at UC Berkeley, subsequently becoming a true “causal inference enthusiast” during a postdoc at Harvard starting in 2007. My research centers broadly on developing and aiding the application of longitudinal causal inference methods grounded in causal questions that inform how to extend lives and improve different aspects of human health. This work has been inspired by a wide range of problems in my collaborations over the years with researchers in nutritional and environmental epidemiology, HIV/AIDS, pregnancy and birth outcomes, critical care, primary care, and more.
It is my honor and privilege to run for SCI’s Secretary. My relevant committee experience includes my past service as Publications Officer for the ASA’s Section on Statistics in Epidemiology and my current role as Co-Chair of my department’s DEI Research Subcommittee. I also served for two years on the Program Committee for the ASA’s StatFest, an annual conference encouraging undergraduate students from historically underrepresented groups to consider careers and graduate studies in statistics. If elected, I would leverage this experience to aid SCI’s efforts in a number of areas, including: 1) advancing opportunities in causal inference training for historically underrepresented students and researchers; 2) ensuring that ACIC is a welcoming and intellectually stimulating experience each year for SCI members, particularly for students and early career stage researchers, and 3) improving communication and fostering collaboration between causal inference researchers across institutions and disciplines.
Youjin Lee
Youjin Lee, PhD is Manning Assistant Professor in the Department of Biostatistics at Brown University. She received her BA in Statistics from Seoul National University in South Korea, her PhD in Biostatistics from Johns Hopkins University, and completed a postdoctoral fellowship at University of Pennsylvania. Dr. Lee’s primary research area is causal inference for observational studies, with a focus on developing replicable and robust methodologies with complex data. Her new causal inference methodologies have been applied to various public health and social science studies with policy implications. Dr. Lee’s work has been published in journals such as Biometrika, Biometrics, the Journal of the American Statistical Association, the Annals of Statistics, and the Journal of the Royal Statistical Society A. She serves as the Associate Editor for Reproducibility for the Journal of the American Statistical Association and as the Program Director Elect of the Korean International Statistical Society.
I am delighted to run for the position of at-large member for the Society for Causal Inference (SCI). Over the past years, it has been my great pleasure to connect with other researchers and professionals in causal inference through the American Causal Inference Conference and the Online Causal Inference Seminar. It would be my great honor to participate in the Executive Committee for SCI to help strengthen those connections in the community. As a (bio)statistician working in academia in the field of public health, I would like to bring public health and social science researchers and professionals from diverse fields into the SCI community through data analysis competitions, workshops, and mentorship opportunities. Furthermore, I am committed to establishing connections with young students from diverse backgrounds through outreach programs aimed at educating them about making informed decisions using data science. I anticipate the opportunity to join the SCI executive team and contribute to the expansion and influence of SCI across diverse fields of causal inference.
Member at Large
Two candidates will be selected.
Shu Xu
My work represents a balance of both statistical and applied aspects of quantitative methodology. My primary quantitative interests include evaluating and developing statistical methods for longitudinal data analysis. Specifically, My research focuses on various aspects of latent growth models, missing data methods, and causal inference models.
I have served as Investigator/Biostatistician on more than 10 federally or locally funded research projects. I was PI of an NIH/NCI supplement award through the University of Michigan/Georgetown Center for the Assessment of the Public Health Impact of Tobacco Regulations (3U54CA229974), and the project aimed to examine the longitudinal effect of e-cigarette exposure on subsequent tobacco use patterns using conventional and causal mediation methods. I was also co-Investigator of an NIH/NCI R21 award (1R21CA260423-01). This project aims to assess the longitudinal impact of e-cigarette flavor, device, and marketing exposure on tobacco use and health outcome using propensity score weighting and causal mediation methods. I am PI of an on-going NIH NIDA/FDA K01 award (1K01DA058408). This project aims to the development and implementation of causal machine learning methods to inform tobacco regulatory sciences.
I have collaborated with substance use, family, and health researchers to advance and share my knowledge of quantitative methodology and pursue a better understanding of the social sciences and public health. I have conducted research with the Family Translational Research Group at New York University and the Methodology Center at the Pennsylvania State University.
Coming Soon!
Samrachana Adhikari
Samrachana Adhikari is Associate Professor in the Department of Population Health (Division of Biostatistics) at NYU Grossman School of Medicine. She received her undergraduate degree in Mathematics and Economics from Mount Holyoke College and PhD in Statistics from Carnegie Mellon University. She completed her postdoctoral fellowship in Healthcare Policy and Biostatistics from Harvard Medical School. Her current methods research focuses on generalizable prediction models, Hierarchical Bayesian latent variable social network analysis, and Bayesian causal inference methods. In terms of substantial application areas, her work spans topics in social and neighborhood determinants of chronic diseases, medication non-adherence, and opioid policy evaluation. In 2020, she was awarded the Johnson & Johnson Women in STEM Scholar award in math. She currently serves as MPI on a NHLBI funded study that aims to utilize social determinants of health data as well as individual factors to develop fair and generalizable algorithm to predict medication non-adherence among heart failure patients.
I am passionate about promoting and facilitating ‘statistical stewardship’ and ‘causal thinking’ in the wider scientific community. Serving on the board of the Society for Causal Inference as a member at large will provide me with the ideal opportunity to work towards this goal of improving access and visibility to the science of causal inference in the wider research community. I am committed to work closely with other board members and accomplish the mission of the society: “to foster the science of causal inference and connect disparate fields that use causal knowledge”.