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About
About
I am an Associate Professor in the Departments of Mathematics and Population Health Sciences at the University of Wisconsin–Madison. My training is in applied mathematics, and I specialize in causal inference. Most of my work is motivated by problems in mental health and emergency medicine, where close collaboration with clinicians often surfaces methodological questions that theory alone would not have found. Lately, answering those questions has meant developing methods that hold up against the ways real clinical data break standard causal inference assumptions.
Email: cochran at wisc dot edu
What's keeping me busy
What's keeping me busy
A few of the projects occupying my attention these days:
Learning health systems. Developing ways to evaluate predictive models as they enter clinical practice, even as new versions continue to replace the ones being studied (MPI of PCORI Methodology Award, Quasi-experimental designs for learning health systems | preprint | code )
Autism prevalence. Applying causal inference methods to study what early life factors might be contributing to the rising prevalence of autism (MPI of NIH OTA, Understanding causes of autism, its heterogeneity, and rising prevalence | website | code )
Unmeasured confounding. Developing methods to address unmeasured confounding in electronic health records (work of former PhD student Haley Colgate Kottler | preprint | old work)
Causal inference for stochastic service systems. Bringing causal inference to settings with a random sequence of events (paper)
Lecture notes for causal inference. Originally developed for a more mathematical course; now being reworked for population health and epidemiology students (mathematical version | work-in-progress applied version)
Power and sample-size tools. Proposing effect size measures for planning studies with generalized linear models (preprint 1 | preprint 2)
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