Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structure

Discovering causal structures in data is a challenging task. Ideally, we would like to input the data into an algorithm that outputs one or more plausible causal directed acyclic graphs (DAGs) linking the variables in the data. Such data driven approaches for estimating a causal DAG are known as causal discovery (or causal search, structure learning etc.). While algorithms for causal discovery were first developed in the field of computer science more than twenty years ago [1] and have, since then, continually been generalised and refined [2], their use with biomedical or epidemiological data is still rare (other than in genetics [3]). Exceptions are, for example, two applications of causal discovery to data from cohort studies finding that modifiable risk factors in early childhood or early life have mostly indirect, if any, causal relations with later health outcomes[4, 5]. Similarly, in an analysis of healthcare data considering cardiac surgery it was found that many of the known predictors were in fact only indirect causes of postoperative length of stay [6]. Also, it has been suggested to use causal discovery to improve the quality of care for hip replacement patients by investigating the complex clinical performance of implants with data from large patient registries [7]. Typical causal analyses often aim at causal effect estimation. In contrast to the above, such analyses typically assume the causal structure, i.e. the DAG, to be given, usually derived from domain