Distinguishing Cause and Effect via Second Order Exponential Models

We propose a method to infer causal structures containing both discrete and continuous variables. The idea is to select causal hypotheses for which the conditional density of every variable, given its causes, becomes smooth. We define a family of smooth densities and conditional densities by second order exponential models, i.e., by maximizing conditional entropy subject to first and second statistical moments. If some of the variables take only values in proper subsets of R^n, these conditionals can induce different families of joint distributions even for Markov-equivalent graphs. We consider the case of one binary and one real-valued variable where the method can distinguish between cause and effect. Using this example, we describe that sometimes a causal hypothesis must be rejected because P(effect|cause) and P(cause) share algorithmic information (which is untypical if they are chosen independently). This way, our method is in the same spirit as faithfulness-based causal inference because it also rejects non-generic mutual adjustments among DAG-parameters.Comment: 36 pages, 8 figure

Causal Inference from Statistical Data

The so-called kernel-based tests of independence are developed for automatic causal discovery between random variables from purely observational statistical data, i.e., without intervention. Beyond the independence relations, the complexity of conditional distriubtions is used as an additional inference principle of determining the causal ordering between variables. Experiments with simulated and real-world data show that the proposed methods surpass the state-of-the-art approaches

Exposure to Polycyclic Aromatic Hydrocarbons, Plasma Cytokines, and Heart Rate Variability

Epidemiological studies have suggested associations between polycyclic aromatic hydrocarbons (PAHs) and heart rate variability (HRV). However, the roles of plasma cytokines in these associations are limited. In discovery stage of this study, we used Human Cytokine Antibody Arrays to examine differences in the concentrations of 280 plasma cytokines between 8 coke-oven workers and 16 community residents. We identified 19 cytokines with significant different expression (fold change ≥2 or ≤−2, and q-value 16% BDNF decreases. Additionally, OH-PAHs were positively associated with activated leukocyte cell adhesion molecule (ALCAM) and C-reactive protein (CRP) (p 20% increases in CRP. We also found significant associations between these cytokines and HRV (p 8% decreases in HRV. Our results indicated PAH exposure was associated with plasma cytokines, and higher cytokines were associated with decreased HRV, but additional human and potential mechanistic studies are needed

Quantitative analysis of late gadolinium enhancement in hypertrophic cardiomyopathy: comparison of diagnostic performance in myocardial fibrosis between gadobutrol and gadopentetate dimeglumine

The purpose of this study was to compare different semi-automated late gadolinium enhancement (LGE) quantification techniques using gadobutrol and gadopentetate dimeglumine contrast agents with regard to the diagnosis of fibrotic myocardium in patients with hypertrophic cardiomyopathy (HCM). Thirty patients with HCM underwent two cardiac MRI protocols with use of gadobutrol and gadopentetate dimeglumine. Contrast-tonoise ratio (CNR) between LGE area and remote myocardium (CNRremote), between LGE area and left ventricular blood pool (CNRpool), and signal-to-noise ratio (SNR) in LGE were compared. The presence and quantity of LGE were determined by visual assessment. With signal threshold versus reference mean (STRM) based thresholds of 2 SD, 5 SD, and 6 SD above the mean signal intensity (SI) of reference myocardium, the full-width at half-maximum (FWHM) technique was used. The volume and segments of the LGE area were compared between the two types of contrast agents. LGE was present in 26 of 30 (86.6%) patients in both protocols. The CNRremote of fibrotic myocardium in gadobutrol and gadopentetate dimeglumine agents was 26.82 ± 14.24 and 21.46 ± 10.59, respectively (P < 0.05). The CNRpool was significantly higher in gadobutrol (9.32 ± 7.64 vs. 6.39 ± 6.11, P < 0.05). The SNR was higher in gadobutrol (33.36 ± 14.35 vs. 27.53 ± 10.91, P < 0.05). The volume of scar size in MR images acquired with gadobutrol were significantly higher than those with gadopentetate dimeglumine (P < 0.05), and the STRM of 5 SD technique showed the greatest agreement with visual assessment (ICC = 0.99) in both examinations. There was no significant difference in fibrotic segments of the fibrotic myocardium in the LGE area (P < 0.05). This study proved that the Gadobutrol was an effective contrast agent for LGE imaging with superior delineation of fibrotic myocardium as compared to gadopentetate dimeglumine. The 5 SD technique yields the closest approximation of the extent of LGE identified by visual assessment

G protein-coupled receptor-mediated calcium signaling in astrocytes

Astrocytes express a large variety of G~protein-coupled receptors (GPCRs) which mediate the transduction of extracellular signals into intracellular calcium responses. This transduction is provided by a complex network of biochemical reactions which mobilizes a wealth of possible calcium-mobilizing second messenger molecules. Inositol 1,4,5-trisphosphate is probably the best known of these molecules whose enzymes for its production and degradation are nonetheless calcium-dependent. We present a biophysical modeling approach based on the assumption of Michaelis-Menten enzyme kinetics, to effectively describe GPCR-mediated astrocytic calcium signals. Our model is then used to study different mechanisms at play in stimulus encoding by shape and frequency of calcium oscillations in astrocytes.Comment: 35 pages, 6 figures, 1 table, 3 appendices (book chapter

Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions

We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities. The motivation is to provide a tool for a causal inference method which assumes that conditional probabilities for effects given their causes are typically simpler and smoother than vice-versa. We present experiments with toy data where the quantitative results are consistent with our intuitive understanding of complexity and smoothness. Also in some examples with real-world data the probability measure corresponding to the true causal direction turned out to be less complex than those of the reversed order

Causal Inference By Choosing Graphs With Most Plausible Markov Kernels

We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the so-called "Markov kernels") for all hypothetical causal directions and choosing the most plausible one. We consider those Markov kernels most plausible, which maximize the (conditional) entropies constrained by their observed first moment (expectation) and second moments (variance and covariance with its direct causes) based on their given domain. In thi

A Kernel-based Causal Learning Algorithm

We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the line of the common faithfulness assumption of constraint-based causal learning, our approach assumes that a variable Z is likely to be a common effect of X and Y, if conditioning on Z increases the dependence between X and Y. Based on this assumption, we collect “votes” for hypothetical causal directions and orient the edges by the majority principle. In most experiments with known causal structures, our method provided plausible results and outperformed the conventional constraint-based PC algorithm. 1