INFORMATION
Current members of the Society are eligible to vote. Voting will begin on March 25, 2024 and will close April 18, 2024.
Nandita Mitra
- Nandita Mitra is Professor of Biostatistics at the University of Pennsylvania. She is also the co-director of the Penn Center for Causal Inference and past Vice Chair of Education, Vice Chair of Faculty Professional Development, and Chair of the Graduate Group in Epidemiology and Biostatistics at Penn. She received her BA in Mathematics from Brown University, MA in Biostatistics from the University of California, Berkeley, PhD in Biostatistics from Columbia University, and completed a postdoctoral fellowship at Harvard.Her primary research area is causal inference with a focus on developing propensity score, instrumental variable, and sensitivity analysis methods for observational data with applications in cancer, health policy, and health economics. She is the Editor-in-Chief of Observational Studies and is a Fellow of the American Statistical AssociationShe has held several relevant leadership roles on national and international committees including SCI Secretary, SCI Co-Chair of Outreach, ENAR Spring Meeting Program Chair, IBS Budget & Finance Chair, and ASA Statistics in Epidemiology Chair.
- I am truly delighted to run for President of the Society for Causal Inference. The mission of the SCI is near and dear to my heart: to bring together researchers across different disciplines to promote the development and application of causal inference methods that have a true impact on individuals, communities, and more globally.As President of SCI, I would like to continue my work as past Secretary of SCI and current Co-chair of the SCI Outreach Committee to enhance the impact and visibility of our society. I would be committed to increasing participation among students, post-docs, and junior investigators and ensure that they are included in the governance of the society. I would also like to prioritize professional development activities such as grant writing workshops, teaching workshops, skill-building courses, and opportunities for one-on-one mentorship with leaders in the field and to enhance outreach to high school and college students to encourage them to pursue a career in fields that rely on causal inference.I am truly grateful for the opportunity to run for this office and look forward to having a true impact on our Society’s future activities supporting innovative and impactful research, fostering collaborations across disciplines, mentoring junior investigators, and increasing diversity in the field.
SCI 2025 Election Candidates
Caleb Miles
- Caleb Miles is an assistant professor of biostatistics at the Columbia University Mailman School of Public Health. He received his PhD in biostatistics from Harvard in 2015, then did a postdoctoral fellowship at UC Berkeley before joining Columbia. He works on developing semiparametric methods for causal inference and applying them to problems in public health. His applied work is largely in HIV/AIDS, drug abuse, psychiatry, and anesthesiology. His methodological research interests include causal inference, its intersection with machine learning, mediation analysis, data fusion, and measurement error. Since arriving at Columbia, he has continuously served as a co-investigator on NIH funded grants, and is currently principal investigator of an R01 to develop methodology for efficiently combining data across multiple studies to study causal effects of medication for opioid use disorder. He is an associate editor for the Journal of the Royal Statistical Society: Series C and the International Journal of Biostatistics.
- It is my honor to be running for member at large of the Society for Causal Inference. I have been working in the field of causal inference since my days as a graduate student, and have been attending ACIC regularly since 2013 (when it was still the Atlantic Causal Inference Conference). I have witnessed the field expand both in its numbers as well as its recognition from the wider scientific community. In this time, the causal inference community has grown to feel like my professional “home”, and for this reason I hope to have the opportunity to serve this community and make sure that it is an inclusive and enriching professional home for all of you as well.If elected member at large, I will work with the Society to make causal inference accessible and welcoming to a diverse range of professionals, from academia to industry, students to senior investigators, and with interests spanning disciplines as well as the spectrum from applied to theoretical. I will work to improve communication between members and the SCI board, and to resolve issues as basic as emails getting caught in spam filters.I am excited and honored to be faced with the possibility of serving the causal inference community—a community that has played a pivotal role in my educational and professional life. I will do my best to ensure that it remains one in which exciting new ideas are nurtured and professional connections and friendships are forged and strengthened.
Andrew Spieker
Andrew Spieker is an Associate Professor of Biostatistics at Vanderbilt University Medical Center. He received his B.S. in Mathematics from Northeastern University, his Ph.D. in Biostatistics from University of Washington, and completed a postdoctoral fellowship at the University of Pennsylvania. His methodological research focus is on causal inference methods, with special emphasis on sensitivity analysis, instrumental variable approaches, and longitudinal treatment regimes. He has received recognition for his methodological research in causal inference, having been awarded the WNAR Student Paper Awards for Most Outstanding Presentation and Most Outstanding Contributed Paper in 2015; he was also a runner-up for the Ten Have Poster Presentation award for ACIC in 2017. He is dedicated to service to the causal inference and broader statistical community; he has served in a number of leadership roles in professional organizations (including as ENAR Chair of Local Arrangements, as Biometrics Program Chair for ASA). He is currently a member of the SCI Professional Development Committee, and an Associate Editor for Observational Studies and the Book Review Editor for Biometrics.
I am honored to be considered to the position of Member at Large of the Society for Causal Inference. I have been a regular attendee of ACIC since 2017. I have previously served as a poster session judge and recently joined the SCI Professional Development Committee.
When asked about what draws me to causal inference, I have two responses: (1) I appreciate that the causal framework forces a researcher to be explicit about assumptions on the front end, and (2) the strong community we have, which is undoubtedly fostered by the interdisciplinary nature of our field. I would be delighted to be able to serve the SCI and the broader causal inference community in the role of Member at Large.
If elected, my priorities would be to work with the SCI leadership to maintain the upward trajectory of our national and international visibility, and to foster an inclusive environment that acknowledges the heterogeneity of our field (by discipline, sector, and area of focus). Ultimately, the goal is for SCI be known internationally as a professional home for methodologists in and practitioners of causal inference.
Laura Forastiere
- Laura Forastiere is an Associate Professor in the Department of Biostatistics at Yale School of Public Health. 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. Laura is eager to apply advanced statistical methodology to provide evidence on effective strategies to improve the health and wellbeing of vulnerable populations. She is particularly interested in exploring behavioral interventions that, relying on theories of behavioral economics and social phycology, exploit social interactions and peer influence among individuals. She is involved in many program evaluations and research studies in low- and middle-income countries on malaria, HIV and other STDs, maternal and child health, nutrition, cognitive development, health insurance and microcredit. Dr. Forastiere received her Ph.D. in statistics from the University of Florence (Italy) and postdoc training in statistics and biostatistics at Harvard University. Prior to joining the Department of Biostatistics at Yale School of Public Health, she was a Postdoctoral Associate in the Yale Institute for Network Science.
I am excited for the opportunity to contribute to the Society for Causal Inference and support its mission of advancing the field. I look forward to collaborating with fellow members to promote methodological innovations, foster interdisciplinary dialogue, and strengthen the impact of causal inference in research and policy.
Karla Dias-Ordaz
- Karla is a Professor of Biostatistics and a Sir Henry Dale fellow based at the Department of Statistical Science in University College London.Karla’s research focuses on the interplay of causal inference with machine learning. This includes theoretical developments, novel methodology, and applied data science in health policy evaluation, pharmacoepidemiology and clinical trials. Her areas of expertise include developing and applying debiased machine learning estimators to problems such as heterogeneous treatment effects, counterfactual prediction, and variable importance.She also actively collaborates on interdisciplinary research projects, spanning health, social sciences, and veterinary studies. She is currently the chair of the European Causal Inference meeting steering group, who organise the annual causal inference conference in Europe.
- I am honored to be nominated for the position “at-large member” of the Society for Causal Inference (SCI) board. I have actively participated in ACIC since 2016, frequently serving as a poster judge, and have been a SCI member since 2021.I am passionate about raising awareness of causal thinking across the natural and mathematical sciences, and I regularly conduct educational workshops around the world, particularly in Europe and Latin America.I believe the SCI has the potential to further strengthen international collaborations and cultivate a global causal inference network. This could be achieved by organizing and promoting educational activities, as well as research workshops and meetings, to maximize the exchange of ideas and advance our discipline.Currently, I serve as co-chair of the steering committee for the European Causal Inference Meeting and as the British and Irish Region council representative for the International Biometrics Society. I am also a member of a UK AI Hub focused on the role of causality in artificial intelligence.If elected, I would leverage these roles to: 1) increase opportunities for idea exchanges with the European causal inference community; and 2) strengthen connections with (i) biostatisticians and other quantitative scientists working in public health and biology, and (ii) the AI community.