Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.Comment: 101 pages, second revision submitted to Journal of Machine Learning Researc

Effects of climate extremes on the terrestrial carbon cycle : concepts, processes and potential future impacts

This article is protected by copyright. All rights reserved. Acknowledgements This work emerged from the CARBO-Extreme project, funded by the European Community’s 7th framework programme under grant agreement (FP7-ENV-2008-1-226701). We are grateful to the Reviewers and the Subject Editor for helpful guidance. We thank to Silvana Schott for graphic support. Mirco Miglivacca provided helpful comments on the manuscript. Michael Bahn acknowledges support from the Austrian Science Fund (FWF; P22214-B17). Sara Vicca is a postdoctoral research associate of the Fund for Scientific Research – Flanders. Wolfgang Cramer contributes to the Labex OT-Med (n° ANR-11- LABX-0061) funded by the French government through the A*MIDEX project (n° ANR-11-IDEX-0001-02). Flurin Babst acknowledges support from the Swiss National Science Foundation (P300P2_154543).Peer reviewedPublisher PD

Reviewing the Carbonation Resistance of Concrete

The paper reviews the studies on one of the important durability properties of concrete i.e. Carbonation. One of the main causes of deterioration of concrete is carbonation, which occurs when carbon dioxide (CO2) penetrates the concrete’s porous system to create an environment with lower pH around the reinforcement in which corrosion can proceed. Carbonation is a major cause of degradation of concrete structures leading to expensive maintenance and conservation operations. Herein, the importance, process and effect of various parameters such as water/cement ratio, water/binder ratio, curing conditions, concrete cover, super plasticizers, type of aggregates, grade of concrete, porosity, contaminants, compaction, gas permeability, supplementary cementitious materials (SCMs)/ admixtures on the carbonation of concrete has been reviewed. Various methods for estimating the carbonation depth are also reported briefl

The effect of univariate bias adjustment on multivariate hazard estimates

Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure.</p

Different interpretations of sufficiency in climate-protection strategies: a typology based on 40 pioneering municipalities in Germany

Sufficiency is a crucial strategy for achieving climate targets by reducing energy and resource consumption in absolute terms through changed practices. While most climate-protection concepts focus almost exclusively on the technological strategies of efficiency and consistency (e.g., renewable energies), sufficiency is being increasingly considered in public policy as a social-organizational strategy, especially at the municipal level. However, given the diverse facets of this theoretical concept, the interpretations of the character of sufficiency vary widely. Using examples from the 40 German Masterplan municipalities, our qualitative study examines these different interpretations of sufficiency in municipal climate-protection concepts. In this study we analyze the general meaning and relevance of sufficiency in the concepts mentioned, work out the central dimensions of sufficiency and use them to distinguish between the different concepts, and present a typology, which allows the basic distinction between four municipal sufficiency types: technophiles, privatizers, vision builders, and frameworkers. The results show that sufficiency is gaining importance for municipal climate protection and can contribute to alternative future visions. However, sufficiency remains mostly subordinated to technological solutions and is hardly woven into the specific sectoral strategies and concrete measures. Furthermore, the transformative trajectories are limited through depoliticized understandings of sufficiency in many cases. We therefore argue for a more political, cross-sectoral, and transformative interpretation of sufficiency as a guiding principle in public climate policy that links tangible framework conditions for sufficiency practices with visions for alternative futures

A submonthly database for detecting changes in vegetation-atmosphere coupling

Land-atmosphere coupling and changes in coupling regimes are important for making precise future climate predictions and understanding vegetation-climate feedbacks. Here we introduce the Vegetation-Atmosphere Coupling (VAC) index which identifies regions and times of concurrent strong anomalies in temperature and photosynthetic activity. The different classes of the index determine whether a location is currently in an energy-limited or water-limited regime, and its high temporal resolution allows to investigate how these regimes change over time at the regional scale. We show that the VAC index helps to distinguish different evaporative regimes. It can therefore provide indirect information about the local soil moisture state. We further demonstrate how the index can be used to understand processes leading to and occurring during extreme climate events, using the 2010 heat wave in Russia and the 2010 Amazon drought as examples

Different interpretations of sufficiency in climate-protection strategies: a typology based on 40 pioneering municipalities in Germany

Diese Arbeit wurde mit Mitteln des Bundesministeriums für Bildung und Forschung (BMBF) im Rahmen der Nachwuchsgruppe "BioKum" [Förderkennzeichen: 031B0751] gefördert. Diese Publikation wurde außerdem aus Mitteln des Publikationsfonds NiedersachsenOpen von zukunft.niedersachsen gefördert.Sufficiency is a crucial strategy for achieving climate targets by reducing energy and resource consumption in absolute terms through changed practices. While most climate-protection concepts focus almost exclusively on the technological strategies of efficiency and consistency (e.g., renewable energies), sufficiency is being increasingly considered in public policy as a social-organizational strategy, especially at the municipal level. However, given the diverse facets of this theoretical concept, the interpretations of the character of sufficiency vary widely. Using examples from the 40 German Masterplan municipalities, our qualitative study examines these different interpretations of sufficiency in municipal climate-protection concepts. In this study we analyze the general meaning and relevance of sufficiency in the concepts mentioned, work out the central dimensions of sufficiency and use them to distinguish between the different concepts, and present a typology, which allows the basic distinction between four municipal sufficiency types: technophiles, privatizers, vision builders, and frameworkers. The results show that sufficiency is gaining importance for municipal climate protection and can contribute to alternative future visions. However, sufficiency remains mostly subordinated to technological solutions and is hardly woven into the specific sectoral strategies and concrete measures. Furthermore, the transformative trajectories are limited through depoliticized understandings of sufficiency in many cases. We therefore argue for a more political, cross-sectoral, and transformative interpretation of sufficiency as a guiding principle in public climate policy that links tangible framework conditions for sufficiency practices with visions for alternative futures

Testing whether linear equations are causal: A free probability theory approach

We propose a method that infers whether linear relations between two high-dimensional variables X and Y are due to a causal influence from X to Y or from Y to X. The earlier proposed so-called Trace Method is extended to the regime where the dimension of the observed variables exceeds the sample size. Based on previous work, we postulate conditions that characterize a causal relation between X and Y. Moreover, we describe a statistical test and argue that both causal directions are typically rejected if there is a common cause. A full theoretical analysis is presented for the deterministic case but our approach seems to be valid for the noisy case, too, for which we additionally present an approach based on a sparsity constraint. The discussed method yields promising results for both simulated and real world data