Wisconsin Autism Data Science Initiative

How We Study Early-Life Factors in Autism

18 May 2026

A research poster was presented at the 2026 Society for Causal Inference Conference (Salt Lake City, Utah). The poster describes how researchers study early-life factors and autism when experiments are not possible. Because researchers cannot ethically or practically conduct experiments during pregnancy or early childhood, understanding these relationships requires careful use of observational data and explicit assumptions. The protocol introduces a transparent, community-informed framework for comparing multiple factors within a single analysis and for interpreting what conclusions can—and cannot—be drawn from the results.

ADSI team member presenting a research poster at the 2026 Society for Causal Inference Conference in Salt Lake City, Utah.
Rachel Hanger presenting a research poster at the 2026 Society for Causal Inference Conference. (Photo credit: Anna Pham.)

Studying Early-Life Factors in Autism

01 May 2026

We presented a research poster at the 2026 International Society for Autism Research (INSAR) Conference (Prague, Czech Republic). This poster applies a new analytic approach to better understand how multiple prenatal, birth, and early-life factors may relate to autism. Rather than examining one factor at a time, the study evaluates many factors together within the same analysis. The goal is to improve our understanding of the complex combination of experiences that may be associated with autism and to inform future research on autism characteristics and support needs.

Research poster presented at the 2026 INSAR conference

The Three Pillars of ADSI

30 Apr 2026

Our approach is built on three pillars:

  • data science,
  • public health, and
  • community engagement.

Data science allows us to make use of large and complex sources of information, while public health helps us understand how individual experiences connect to broader population patterns, resources, and health systems. Community engagement ensures that autistic individuals, families, clinicians, and advocates have a meaningful role in shaping the research process. By bringing these perspectives together, we aim to produce research that is both methodologically rigorous and responsive to the priorities, concerns, and lived experiences of the autism community.

Triangle diagram illustrating the three pillars of the ADSI research approach. Data Science contributes data, analytics, and causal models; Public Health contributes understanding of population patterns, resources, and health systems; and Community Engagement contributes lived experience, partnerships, and Community Advisory Board input. Together, these pillars support autism research.
UW–Madison ADSI brings together data science, public health, and community engagement to support rigorous, community-informed autism research.