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Fan Li

Associate Professor at Department of Statistical Science, Duke University

Fan Li got a BSc in Mathematics from Peking University in 2001, and PhD in Biostatistics from Johns Hopkins University in 2006. Before joining Duke in 2008, She did a postdoctoral fellowship at Harvard Medical School Department of Health Care Policy. Here main research interest in statistical methodology is causal inference, that is, designs and…

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Fan Li got a BSc in Mathematics from Peking University in 2001, and PhD in Biostatistics from Johns Hopkins University in 2006. Before joining Duke in 2008, She did a postdoctoral fellowship at Harvard Medical School Department of Health Care Policy. Here main research interest in statistical methodology is causal inference, that is, designs and methods of analyses to evaluate treatments, interventions or actions in randomized experiments or observational studies, and their applications to social sciences, economics, health policy, epidemiology and engineering. She also have a strong interest in statistical methods for big and complex data, such as neuroimaging data, with an emphasis on developing advanced Bayesian inferential and computational methods. She also work on missing data, variable selection, and small area estimation.

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Fan Li will discuss a few challenging problems in causal inference emerging from the era of precision medicine and machine learning. Some topics will include:
1) What do we mean by precision medicine? Prediction vs. Causal?
2) What do we mean by machine learning (ML)? Is ML a magic bullet?
3) What type of problems in precision medicine that ML is mostly effective (compared to traditional methods like regression)?
4) Different frameworks of causal inference: is this a conflict of ideology and/or personality rather than substance? Does it really matter in practice?

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