Centre for Statistical Methodology seminar series - missing data and causal inference theme
There is an increased awareness of the importance of non-parametrically defining the causal effect measure of interest prior to any modelling of the data. This is relatively easily done when the exposure is binary, but much less straightforward when continuous exposures are considered.
Causal inference in a time of coronavirus: tenofovir, tocilizumab, hydroxychloroquine Speaker
Miguel Hernán MD PhD (Kolkotrones Professor, Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health)
Please note that the time listed is Greenwich Mean Time (GMT)
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