Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework

Principled reasoning about the identifiability of causal effects from non-experimental data is an important application of graphical causal models. This paper focuses on effects that are identifiable by covariate adjustment, a commonly used estimation approach. We present an algorithmic framework for efficiently testing, constructing, and enumerating $m$-separators in ancestral graphs (AGs), a class of graphical causal models that can represent uncertainty about the presence of latent confounders. Furthermore, we prove a reduction from causal effect identification by covariate adjustment to $m$-separation in a subgraph for directed acyclic graphs (DAGs) and maximal ancestral graphs (MAGs). Jointly, these results yield constructive criteria that characterize all adjustment sets as well as all minimal and minimum adjustment sets for identification of a desired causal effect with multivariate exposures and outcomes in the presence of latent confounding. Our results extend several existing solutions for special cases of these problems. Our efficient algorithms allowed us to empirically quantify the identifiability gap between covariate adjustment and the do-calculus in random DAGs and MAGs, covering a wide range of scenarios. Implementations of our algorithms are provided in the R package dagitty.Comment: 52 pages, 20 figures, 12 table

Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs

Causal effect estimation from observational data is a fundamental task in empirical sciences. It becomes particularly challenging when unobserved confounders are involved in a system. This paper focuses on front-door adjustment -- a classic technique which, using observed mediators allows to identify causal effects even in the presence of unobserved confounding. While the statistical properties of the front-door estimation are quite well understood, its algorithmic aspects remained unexplored for a long time. In 2022, Jeong, Tian, and Bareinboim presented the first polynomial-time algorithm for finding sets satisfying the front-door criterion in a given directed acyclic graph (DAG), with an $O(n^3(n+m))$ run time, where $n$ denotes the number of variables and $m$ the number of edges of the causal graph. In our work, we give the first linear-time, i.e., $O(n+m)$, algorithm for this task, which thus reaches the asymptotically optimal time complexity. This result implies an $O(n(n+m))$ delay enumeration algorithm of all front-door adjustment sets, again improving previous work by a factor of $n^3$. Moreover, we provide the first linear-time algorithm for finding a minimal front-door adjustment set. We offer implementations of our algorithms in multiple programming languages to facilitate practical usage and empirically validate their feasibility, even for large graphs.Comment: Extended version of paper accepted to the Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-2024

Mentorship narratives in a local congregation : a postfoundational practical theological study

In this study narratives of mentorship are listened to and described from the local context of the Dutch Reformed Church Lynnwood. These narratives originate from the mentorship programme in the youth ministry of the congregation. The research approach in this study flows from an epistemology based on narrative theory, social constructionism and a postfoundational approach. It is a practical theological study that aims to come to a greater understanding of these narratives. Based on the epistemology discussed here, I position myself within the framework of a postfoundational practical theology. Within this framework the praxis is the starting point of this research. This is local knowledge, interpreted and described by a community of co-researchers as informed by traditions of interpretation. The in-context experiences are interpreted and thickened through interdisciplinary investigation. This is done through a study of relevant literature as well as interdisciplinary discussion based on the theory of transversal rationality. At the end of this research process, alternative interpretations and suggestions are made that point beyond this local context and contribute to the larger field of mentorship. The research process in this study is developed from the postfoundational practical theological positioning. Seven movements are used to listen to the narratives of the eight co-researchers participating in this study. The narratives of the co-researchers lead to the identification of certain themes from their experiences that resonate with themes available to us in literature. The same themes also emerge from the interdisciplinary conversation in this study. These themes are critically discussed and certain questions are raised with regards to mentorship and the way mentorship is understood in different contexts. The issue of language and how the various fields concerned with mentorship use language is discussed. It is clear from this study that the local context of mentorship in this study differs in certain aspects from other contexts where mentorship is practiced. The difference between coaching and mentorship is investigated and reflected upon. I argue in the concluding chapter that from this context three basic foundation blocks for mentorship emerge. Firstly, the relationship forms the first basic building block of mentorship. Secondly, growth is the second basic building block and also the aim of mentorship. Although the way growth is understood may vary from context to context, it still forms one of the basic building blocks of mentoring relationships. The third basic building block is the fact that the mentorship relationship is reciprocal. I suggest an approach to mentorship that emerged from the narratives of the local praxis studied. This approach is based on values. The following values are suggested as necessary in a meaningful mentorship programme or relationship: clarity, context, the ordinary, relationship, listening, adding value, reflection and ethics. I conclude that mentorship is a landscape with many voices. The values suggested in this study can be used to construct the understanding of mentorship in a specific, local context. This is done with the aim to facilitate a meaningful mentorship programme or relationship.Thesis (PhD)--University of Pretoria, 2009.Practical Theologyunrestricte

Robust causal inference using directed acyclic graphs: the R package ‘dagitty’

Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package ‘dagitty’, which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions. We describe how the R package ‘dagitty’ can be used to: evaluate whether a DAG is consistent with the dataset it is intended to represent; enumerate ‘statistically equivalent’ but causally different DAGs; and identify exposure outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality enables epidemiologists to detect causal misspecifications in DAGs and make robust inferences that remain valid for a range of different DAGs. The R package ‘dagitty’ is available through the comprehensive R archive network (CRAN) at [https://cran.r-project.org/web/packages/dagitty/]. The source code is available on github at [https://github.com/jtextor/dagitty]. The web application ‘DAGitty’ is free software, licensed under the GNU general public licence (GPL) version 2 and is available at [http:// dagitty.net/]

Artificial intelligence for characterization of diminutive colorectal polyps::A feasibility study comparing two computer-aided diagnosis systems

BACKGROUNDArtificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.AIMTo evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODSAI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTSAI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.CONCLUSIONReal-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.van der Zander QEW, Schreuder RM, Thijssen A, Kusters CHJ, Dehghani N, Scheeve T, Winkens B, van der Ende - van Loon MCM, de With PHN, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. Artif Intell Gastrointest Endosc 2024; 5(1): 90574 [DOI: 10.37126/aige.v5.i1.90574

Untargeted urine metabolomics reveals a biosignature for muscle respiratory chain deficiencies

Mitochondrial diseases are a heterogeneous group of disorders characterised by impaired mitochondrial oxidative phosphorylation system. Most often for mitochondrial disease, where no metabolic diagnostic biomarkers exist, a deficiency is diagnosed after analysing the respiratory chain enzymes (complexes I-IV) in affected tissues or by identifying one of an ever expanding number of DNA mutations. This presents a great challenge to identify cases to undergo the invasive diagnostic procedures required. An untargeted liquid chromatography mass spectrometry metabolomics approach was used to search for a metabolic biosignature that can distinguish respiratory chain deficient (RCD) patients from clinical controls (CC). A cohort of 37 ethnically diverse cases was used. Sample preparation, liquid chromatography time-of-flight mass spectrometry methods and data processing methods were standardised. Furthermore the developed methodology used reverse phase chromatography in conjunction with positive electrospray ionisation and hydrophilic interaction chromatography with negative electrospray ionisation. Urine samples of 37 patients representing two different experimental groups were analysed. The two experimental groups comprised of patients with confirmed RCDs and CC. After a variety of data mining steps and statistical analyses a list of 12 features were compiled with the ability to distinguish between patients with RCDs and CC. Although the features of the biosignature needs to be identified and the biosignature validated, this study demonstrates the value of untargeted metabolomics to identify a metabolic biosignature to possibly be applied in the selection criteria for RCDs.North-West University, Potchefstroom Campushttp://link.springer.com/journal/113062016-02-28hb201

Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared with intuitive optical diagnosis

Background Optical diagnosis of colorectal polyps remains challenging. Image-enhancement techniques such as narrow-band imaging and blue-light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high-definition white-light (HDWL) and BLI images, and compared the system with the optical diagnosis of expert and novice endoscopists.Methods CADx characterized colorectal polyps by exploiting artificial neural networks. Six experts and 13 novices optically diagnosed 60 colorectal polyps based on intuition. After 4 weeks, the same set of images was permuted and optically diagnosed using the BLI Adenoma Serrated International Classification (BASIC).Results CADx had a diagnostic accuracy of 88.3% using HDWL images and 86.7% using BLI images. The overall diagnostic accuracy combining HDWL and BLI (multimodal imaging) was 95.0%, which was significantly higher than that of experts (81.7%, P =0.03) and novices (66.7%, P <0.001). Sensitivity was also higher for CADx (95.6% vs. 61.1% and 55.4%), whereas specificity was higher for experts compared with CADx and novices (95.6% vs. 93.3% and 93.2%). For endoscopists, diagnostic accuracy did not increase when using BASIC, either for experts (intuition 79.5% vs. BASIC 81.7%, P =0.14) or for novices (intuition 66.7% vs. BASIC 66.5%, P =0.95).Conclusion CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of colorectal polyps. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared with intuitive optical diagnosis

Nesiritide: Harmful or Harmless?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90328/1/phco.26.10.1465.pd