Telling Cause from Effect using MDL-based Local and Global Regression

We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables $X$ and $Y$ from observational data. The two-variable case is especially difficult to solve since it is not possible to use standard conditional independence tests between the variables. To tackle this problem, we follow an information theoretic approach based on Kolmogorov complexity and use the Minimum Description Length (MDL) principle to provide a practical solution. In particular, we propose a compression scheme to encode local and global functional relations using MDL-based regression. We infer $X$ causes $Y$ in case it is shorter to describe $Y$ as a function of $X$ than the inverse direction. In addition, we introduce Slope, an efficient linear-time algorithm that through thorough empirical evaluation on both synthetic and real world data we show outperforms the state of the art by a wide margin.Comment: 10 pages, To appear in ICDM1

Information-Theoretic Causal Discovery

It is well-known that correlation does not equal causation, but how can we infer causal relations from data? Causal discovery tries to answer precisely this question by rigorously analyzing under which assumptions it is feasible to infer causal networks from passively collected, so-called observational data. Particularly, causal discovery aims to infer a directed graph among a set of observed random variables under assumptions which are as realistic as possible. A key assumption in causal discovery is faithfulness. That is, we assume that separations in the true graph imply independencies in the distribution and vice versa. If faithfulness holds and we have access to a perfect independence oracle, traditional causal discovery approaches can infer the Markov equivalence class of the true causal graph---i.e., infer the correct undirected network and even some of the edge directions. In a real-world setting, faithfulness may be violated, however, and neither do we have access to such an independence oracle. Beyond that, we are interested in inferring the complete DAG structure and not just the Markov equivalence class. To circumvent or at least alleviate these limitations, we take an information-theoretic approach. In the first part of this thesis, we consider violations of faithfulness that can be induced by exclusive or relations or cancelling paths, and develop a weaker faithfulness assumption, called 2-adjacency faithfulness, to detect some of these mechanisms. Further, we analyze under which conditions it is possible to infer the correct DAG structure even if such violations occur. In the second part, we focus on independence testing via conditional mutual information (CMI). CMI is an information-theoretic measure of dependence based on Shannon entropy. We first suggest estimating CMI for discrete variables via normalized maximum likelihood instead of the plug-in maximum likelihood estimator that tends to overestimate dependencies. On top of that, we show that CMI can be consistently estimated for discrete-continuous mixture random variables by simply discretizing the continuous parts of each variable. Last, we consider the problem of distinguishing the two Markov equivalent graphs X to Y and Y to X, which is a necessary step towards discovering all edge directions. To solve this problem, it is inevitable to make assumptions about the generating mechanism. We build upon the idea which states that the cause is algorithmically independent of its mechanism. We propose two methods to approximate this postulate via the Minimum Description Length (MDL) principle: one for univariate numeric data and one for multivariate mixed-type data. Finally, we combine insights from our MDL-based approach and regression-based methods with strong guarantees and show we can identify cause and effect via L0-regularized regression

Thymoma and Thymic Carcinoma: Molecular Pathology and Targeted Therapy

Abstract:Thymomas and thymic carcinomas (TC) are rare epithelial tumors of the thymus. Although most thymomas have organotypic features (i.e., resemble the normal thymus), TC are morphologically undistinguishable from carcinomas in other organs. Apart from their different morphology, TC and thymomas differ also in functional terms (TC, in contrast to thymomas, have lost the capacity to promote the maturation of intratumorous lymphocytes), have different genetic features (discussed in this review), a different immunoprofile (most TC overexpress c-KIT, whereas thymomas are consistently negative), and different clinical features (TC, in contrast to thymomas, are not associated with paraneoplastic myasthenia gravis). Thus, although all the data suggest that the biology of thymomas and TC is different, in clinical practice, their therapeutic management up to now is identical. In the age of personalized medicine, the time may have come to think this over. We will briefly review the molecular genetics of malignant thymic tumors, summarize the current status of targeted therapies with an emphasis on the multitargeted kinase inhibitors sunitinib and sorafenib, and try to outline some future directions

Chip-based liver equivalents for toxicity testing - organotypicalness versus cost-efficient high throughput

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Drug-induced liver toxicity dominates the reasons for pharmaceutical product ban, withdrawal or non-approval since the thalidomide disaster in the late-1950s. Hopes to finally solve the liver toxicity test dilemma have recently risen to a historic level based on the latest progress in human microfluidic tissue culture devices. Chip-based human liver equivalents are envisaged to identify liver toxic agents regularly undiscovered by current test procedures at industrial throughput. In this review, we focus on advanced microfluidic microscale liver equivalents, appraising them against the level of architectural and, consequently, functional identity with their human counterpart in vivo. We emphasise the inherent relationship between human liver architecture and its drug-induced injury. Furthermore, we plot the current socio-economic drug development environment against the possible value such systems may add. Finally, we try to sketch a forecast for translational innovations in the field

Fluorescent optical fiber sensors for cell viability monitoring

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.A new simple method for non-invasive cell culture viability monitoring based on vital fluorescent stains is introduced, and its efficiency for long-term experiments on cells is demonstrated. In contrast to common methods for cell viability control, which are usually either destructive (like flow-type counters or dead cells coloring and counting), or hardly quantitative like fluorescent microscopy, the method described is automated, does not require the removal of cells from their growth area and is sensitive enough to deal with as low as tens of cells

On the Identifiability and Estimation of Causal Location-Scale Noise Models

We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i.e., $Y = f(X) + g(X)N$. Despite the generality of the model class, we show the causal direction is identifiable up to some pathological cases. To empirically validate these theoretical findings, we propose two estimators for LSNMs: an estimator based on (non-linear) feature maps, and one based on neural networks. Both model the conditional distribution of $Y$ given $X$ as a Gaussian parameterized by its natural parameters. When the feature maps are correctly specified, we prove that our estimator is jointly concave, and a consistent estimator for the cause-effect identification task. Although the the neural network does not inherit those guarantees, it can fit functions of arbitrary complexity, and reaches state-of-the-art performance across benchmarks.Comment: ICML 202