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.