Logics for causal inference under uncertainty

Harmelen, F.A.H. van [Promotor]Groth, P. [Copromotor]Mooij, J.M. [Copromotor

Towards Reconstructing the Provenance of Clinical Guidelines

Abstract. Understanding the provenance of clinical guidelines is important for both practitioners and researchers as it allows for deeper understanding of the provided recommendations and could potentially provide a basis for updating guidelines. Often such provenance is incomplete or unavailable. We describe a prototype of a multi-signal pipeline for reconstructing provenance and show preliminary results of reconstructing dependencies between documents in the context of clinical guidelines and associated documents. 1 Prototype description Broadly, we target the problem of reconstructing provenance of files in a shared folder setting, in which several authors can create or edit files at different moments, and only standard filesystem metadata is available. In a previous work [3] we proposed a content-based approach that is able to reconstruct provenance automatically, leveraging several similarity measures and edit distance algorithms, which are then adapted and integrated them into a multi-signal pipeline. Here, we present an improved version of this prototype applied to a clinical guideline and associated biomedical documents. The architecture of our prototype is shown in Fig. 1. <2,4

Joint Causal Inference from Multiple Contexts

The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach to causal discovery from multiple data sets from different contexts that elegantly unifies both approaches. JCI is a causal modeling framework rather than a specific algorithm, and it can be implemented using any causal discovery algorithm that can take into account certain background knowledge. JCI can deal with different types of interventions (e.g., perfect, imperfect, stochastic, etc.) in a unified fashion, and does not require knowledge of intervention targets or types in case of interventional data. We explain how several well-known causal discovery algorithms can be seen as addressing special cases of the JCI framework, and we also propose novel implementations that extend existing causal discovery methods for purely observational data to the JCI setting. We evaluate different JCI implementations on synthetic data and on flow cytometry protein expression data and conclude that JCI implementations can considerably outperform state-of-the-art causal discovery algorithms.Comment: Final version, as published by JML