Daniel Almirall

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Daniel Almirall, Ph.D., is Assistant Professor in the Survey Research Center and Faculty Associate in the Population Studies Center in the Institute for Social Research at the University of Michigan.

Prof. Almirall’s current methodological research interests lie in the broad area of causal inference. He is particularly interested in methods for causal inference using longitudinal data sets in which treatments, covariates, and outcomes are all time-varying. He is also interested in developing statistical methods that can be used to form adaptive interventions, sometimes known as dynamic treatment regimes. An adaptive intervention is a sequence of individually tailored decisions rules that specify whether, how, and when to alter the intensity, type, or delivery of treatment at critical decision points in the medical care process. Adaptive interventions are particularly well-suited for the management of chronic diseases, but can be used in any clinical setting in which sequential medical decision making is essential for the welfare of the patient. They hold the promise of enhancing clinical practice by flexibly tailoring treatments to patients when they need it most, and in the most appropriate dose, thereby improving the efficacy and effectiveness of treatment.

Study Design Interests: In addition to developing new statistical methodologies, Prof. Almirall devotes a portion of his research to the design of sequential multiple assignment randomized trials (SMARTs). SMARTs are randomized trial designs that give rise to high-quality data that can be used to develop and optimize adaptive interventions.

Substantive Interests: As an investigator and methodologist in the Institute for Social Research, Prof. Almirall takes part in research in a wide variety of areas of social science and treatment (or interventions) research. He is particularly interested in the substantive areas of mental health (depression, anxiety) and substance abuse, especially as related to children and adolescents.

Hongwei Xu

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My substantive research interest is to understand the role of geography in shaping population health. Towards this end, my methodological and data science interests are twofold. First, I seek to develop and apply spatial statistical methods to model individual- and area-level health and diseases by using survey data and government statistics. Second, in light of the advance in GIS techniques and the increasingly accessible spatial data from various sources, I am exploring new approaches to integrate traditional geo-referenced survey data with non-traditional spatial data (e.g., remote sensing data, satellite data, Google search) to reduce measurement errors in demographic health research.

Pamela Giustinelli

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I am interested in modeling, empirical, and counterfactual policy analysis of individual and multilateral decision making under uncertainty-ambiguity, especially as it applies to the family and human capital contexts. I am also interested in survey methodology, particularly as it relates to this line of research. Here are some important questions in my research agenda:

  • How do preferences, beliefs, choice sets, and other elements of a choice situation determine what choices people make and also how they make those choices? (That is, the “decision rules,” “decision protocols,” or “modes of interactions” they use.) And how are those elements formed?
  • What information do individuals and groups have or use when making decisions under uncertainty? And what information is or is not shared among decision makers in multilateral settings?
  • What are the implications of the above points for policy?
  • To inform modeling, identification, and prediction of choice behaviors, what components of individuals’ and groups’ decision processes can we sensibly measure in surveys? From whom? And in what formats?

Data science methodology: Survey design for elicitation of components of human decision processes and interactions under uncertainty/ambiguity

Data science applications: Human capital (school choice, labor supply, end-of-life living arrangements)


Daniel Brown

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Research interests focus on land use change and its effects on ecosystems and on human vulnerability. This work connects simulation (e.g., agent-based modeling) of land-use-change processes with GIS and remote sensing based data on historical patterns of landscape change and social surveys. We are also working to understand the ways in which land-use decisions are made and to evaluate consequences of change.