Postdoctoral Associate
School of Public Health - Biostatistics
Forastiere, Laura
Medical School Campus
Full time
68500
During this appointment, you will work on the development of new causal inference methods in the areas of interference, social networks, causal mediation, principal stratification, complex outcomes, and causal machine learning for understanding treatment effect heterogeneity. Potential projects include: (A) causal inference in randomized experiments for treatment and spillover effects on networks when networks are measured with error; (B) causal inference in complex observational studies, with a time, space, and/or network component; (C) causal
mediation analysis to disentangle spillover effects from other’s mediators and non-compliance; (D) design and analysis of experiments involving network data; (E) optimal targeting strategies under heterogeneous interference.
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