Causal Inference in Statistics and the Quantitative Sciences (09w5043)
Description
In performing inference, statisticians attempt to find associations between variables. Typically, however, it is not only association but causation which is of interest, that is, the statistician would like to say whether some exposure causes a particular outcome. Causal inference is an area of statistics in which methods are developed with the goal of uncovering the underlying structure of the data so as to eliminate all non-causative explanations for an observed association. Causal inference is a highly inter-disciplinary field, with important methodological and theoretical contributions from areas such the computer sciences and economics.
Our five-day workshop will review recent advances in the causal inferences in statistic and bring together researchers from the quantitative sciences who work on causal inference methodology so that knowledge may be shared. Finally, we hope that this workshop will increase attention on causal inference amongst Canadian statisticians and other researchers.
The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada\'s Natural Science and Engineering Research Council (NSERC), the US National Science Foundation (NSF), Alberta\'s Advanced Education and Technology, and Mexico\'s Consejo Nacional de Ciencia y Tecnología (CONACYT).
