STATISTICS WINTER WORKSHOP 2019
Recent Advances in Causal Inference and Mediation Anyalysis and their Applications.
The workshop will focus on recent advances in causal inference and causal mediation analysis. Causal inference is essential for comparative effectiveness research and causal discoveries from (large) observational data (including EHRs). Causal mediation helps us understand how an exposure or intervention works through different pathways. Both become considerably more complex in the presence of interference, on networks, with time-varying exposures, and in big data settings with many potential confounders and/or many potential mediators. Important issues to be addressed include the complication of causal inference in the presence of interference, causal inference on networks, causal mediation analysis in the presence of many mediators, variable selection, sensitivity analysis for uncheckable assumptions (including unmeasured confounders) with applications to ‘omics, mental health, education, networks, and more.
Ten national and international researchers on causal inference will report on their latest research.