Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction

Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.

Causal graph analysis of COVID-19 observational data in German districts reveals effects of determining factors on reported case numbers

Several determinants are suspected to be causal drivers for new cases of COVID-19 infection. Correcting for possible confounders, we estimated the effects of the most prominent determining factors on reported case numbers. To this end, we used a directed acyclic graph (DAG) as a graphical representation of the hypothesized causal effects of the determinants on new reported cases of COVID-19. Based on this, we computed valid adjustment sets of the possible confounding factors. We collected data for Germany from publicly available sources (e.g. Robert Koch Institute, Germany’s National Meteorological Service, Google) for 401 German districts over the period of 15 February to 8 July 2020, and estimated total causal effects based on our DAG analysis by negative binomial regression. Our analysis revealed favorable effects of increasing temperature, increased public mobility for essential shopping (grocery and pharmacy) or within residential areas, and awareness measured by COVID-19 burden, all of them reducing the outcome of newly reported COVID-19 cases. Conversely, we saw adverse effects leading to an increase in new COVID-19 cases for public mobility in retail and recreational areas or workplaces, awareness measured by searches for “corona” in Google, higher rainfall, and some socio-demographic factors. Non-pharmaceutical interventions were found to be effective in reducing case numbers. This comprehensive causal graph analysis of a variety of determinants affecting COVID-19 progression gives strong evidence for the driving forces of mobility, public awareness, and temperature, whose implications need to be taken into account for future decisions regarding pandemic management.

DCI: learning causal differences between gene regulatory networks

Summary Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Availability and implementation Python package freely available at http://uhlerlab.github.io/causaldag/dci. Supplementary information Supplementary data are available at Bioinformatics online.

Fusing Damage Proxy Maps with Geospatial Models for Bayesian Updating of Seismic Ground Failure Estimations: A Case Study in Central Italy

<p>On August 24, 2016, a magnitude-6.2 earthquake in Central Italy resulted in at least 290 deaths, significant ground failure (including landslides and liquefaction), and building damage. After the event, the NASA Advanced Rapid Imaging and Analysis team produced Damage Proxy Maps (DPM) that reflect earthquake-induced surficial changes using synthetic aperture data from the COSMO-SkyMed satellite. However, exact causes of these surface changes, e.g., ground failure, building damage, or other environmental changes, are difficult to directly differentiate from the satellite images alone. For example, changes could reflect building damage, landslides, the co-occurrence of both, or numerous other processes that are not related to the earthquake. Alternatively, existing ground failures models are useful in locating areas of higher likelihoods but suffer from high false alarm rates due to inaccurate or incomplete geospatial proxies and complex physical interdependencies between shaking and specific sites of ground failure. In this work, we present a joint Bayesian updating framework using a causal graph strategy. The Bayesian causal graph models physical interdependencies among ground shaking, ground failures, building damage, and remote sensing observations. Based on the graph, a variational inference approach is designed to jointly update the estimates of ground failure and building damage through fusing traditional geospatial models and the remotely sensed data. As a case study, the DPMs in Central Italy are input to the model for jointly calibrating and updating the probability of ground failure estimations as well as for estimating building damage probabilities. The results showed that by incorporating high-resolution imagery, our model significantly reduces the false alarm rate of ground failure estimates and improves the spatial accuracy and resolution of ground failure and building damage inferences.</p>

A case study and proposal for publishing directed acyclic graphs: The effectiveness of the quadrivalent HPV vaccine in perinatally HIV exposed girls

Background: Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. Recent findings have suggested that the human papillomavirus (HPV) vaccine is less effective in protection against HPV associated disease in a population of girls living with HIV. Development: In order to understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection. Application: We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions prior to data analysis and exemplify the process for designing an etiologic study.Conclusion: The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research prior to undergoing data collection and/or analysis.

DCI: Learning Causal Differences between Gene Regulatory Networks

SummaryDesigning interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale expression datasets from different conditions, cell types, disease states and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we propose an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e., edges that appeared, disappeared or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to bulk and single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions.Availability and implementationAll algorithms are freely available as a Python package at http://uhlerlab.github.io/causaldag/[email protected]

Identifiability from a Combination of Observations and Experiments

We study the problem of causal identification from an arbitrary collection of observational and experimental distributions, and substantive knowledge about the phenomenon under investigation, which usually comes in the form of a causal graph. We call this problem g-identifiability, or gID for short. In this paper, we introduce a general strategy to prove non-gID based on thickets and hedgelets, which leads to a necessary and sufficient graphical condition for the corresponding decision problem. We further develop a procedure for systematically computing the target effect, and prove that it is sound and complete for gID instances. In other words, the failure of the algorithm in returning an expression implies that the target effect is not computable from the available distributions. Finally, as a corollary of these results, we show that do-calculus is complete for the task of g-identifiability.