Xiao-Hua Zhou

PKU Endowed Chair Professor, Beijing International Center for
Mathematical Research, Chair, Department of Biostatistics, Peking
University

Some
Statistical Methods in Causal Inferences and Diagnostic Medicine

Two important areas in biostatistics are causal inference and statistical methods in diagnostic medicine. In this talk, I give an overview on my research interests in these two areas. Particularly, I discuss some new developments in the statistical methodology for making causal inference, and discuss some future research directions. In addition, I give an overview on some new developments in statistical methods in evaluation of the accuracy of medical devices.

Christine Winther Bang

PhD student at Leibniz Institute for Prevention Research and
Epidemiology - BIPS

Improving
causal discovery with temporal background knowledge

Causal discovery methods aim to estimate a (causal) graph from data.
These methods have well-known issues: The output in form of an estimated
equivalence class (represented by a so-called CPDAG) can be sensitive to
statistical errors and is often not very informative. Including
background knowledge, if correct, can only improve (and never harm) the
result of causal discovery. This talk will focus on temporal background
knowledge as would be available in longitudinal or cohort studies, but
the results presented here are valid for any kind of data that has a
tiered ordering of the variables. This type of background knowledge is
reliable, straightforward to incorporate, and the resulting estimated
graphs have desirable properties.

First, I will describe how to
incorporate temporal background knowledge in a causal discovery
algorithm, and provide a practical example of how it can be applied to
cohort data. This algorithm outputs restricted equivalence classes
(represented by so-called tiered MPDAGs) that are more informative, and
more robust to statistical errors compared to CPDAGs.

Second, I
will show how tiered MPDAGs can be characterised as distinct from MPDAGs
based on other types of background knowledge, and how this allows us to
determine exactly when temporal knowledge adds new information, and when
it is redundant. Finally, I will show that this class of graphs inherits
key properties of CPDAGs so that they retain the usual interpretation as
well as computational efficiency.

You can find CSS next to the Botanical Garden, 5 minutes from NÃ¸rreport station.

Meeting room 5.2.46 is the library of the Biostatistics section, located in building 5, 2nd floor, room 46. See the map below for directions inside CSS.