Causal graphs are useful tools for understanding complex causal structures and can help us deciding on interventions or identifying causal effects. Causal graphs are usually constructed based on expert knowledge, which may be insufficient or incorrect. Causal discovery methods construct causal graphs in a data-driven way and serve as an alternative to the classical expert-driven approach but come with other limitations. Typically, without strong assumptions, we cannot identify all causal directions. Moreover, causal discovery methods are sensitive to errors based on e.g. statistical testing when applied to real data. In practice, we often have more causal information available than what can be obtained from the data alone, and the use of so-called background knowledge in causal discovery is a way to bridge the purely data-driven and the purely expert-driven approaches. Time structure induces a partial causal ordering of the variables, which I will refer to as tiered background knowledge. This type of background knowledge is common, e.g. for cohort data, and it improves causal discovery methods such that the output becomes more informative and reliable.In this talk, I will first explain how tiered background knowledge can be incorporated in constraint-based causal discovery algorithms. Then, I will show how this improves graphs estimated using finite sample data. In addition, I will show which time structures yield the most informative graphs and that the output graphs have some desirable theoretical properties. Finally, I will show in which ways tiered background knowledge is useful if we allow for unobserved confounding and consider multiple datasets with overlapping variables.
