scholarly journals Integrating Overlapping Datasets Using Bivariate Causal Discovery

2020 ◽  
Vol 34 (04) ◽  
pp. 3781-3790
Author(s):  
Anish Dhir ◽  
Ciaran M. Lee
Keyword(s):  
Conditional Independence ◽  
Causal Structure ◽  
Real Data ◽  
Causal Discovery ◽  
Causal Relations ◽  
Multiple Datasets ◽  
Single Dataset ◽  
Markov Equivalence ◽  
Causal Structures ◽  
Discovery Algorithms

Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can discover causal relations from observational data are based on the assumption that all variables have been jointly measured in a single dataset. In many cases this assumption fails. Previous approaches to overcoming this shortcoming devised algorithms that returned all joint causal structures consistent with the conditional independence information contained in each individual dataset. But, as conditional independence tests only determine causal structure up to Markov equivalence, the number of consistent joint structures returned by these approaches can be quite large. The last decade has seen the development of elegant algorithms for discovering causal relations beyond conditional independence, which can distinguish among Markov equivalent structures. In this work we adapt and extend these so-called bivariate causal discovery algorithms to the problem of learning consistent causal structures from multiple datasets with overlapping variables belonging to the same generating process, providing a sound and complete algorithm that outperforms previous approaches on synthetic and real data.

The Causal Structure of Suppressor Variables

2019 ◽  
Vol 44 (4) ◽  
pp. 367-389 ◽  
Author(s):  
Yongnam Kim
Keyword(s):  
Instrumental Variables ◽  
Regression Models ◽  
Predictive Power ◽  
Causal Explanation ◽  
Causal Structure ◽  
Criterion Variable ◽  
Psychological Measurement ◽  
Causal Structures ◽  
Discovery Algorithms ◽  
Suppression Effects

Suppression effects in multiple linear regression are one of the most elusive phenomena in the educational and psychological measurement literature. The question is, How can including a variable, which is completely unrelated to the criterion variable, in regression models significantly increase the predictive power of the regression models? In this article, we view suppression from a causal perspective and uncover the causal structure of suppressor variables. Using causal discovery algorithms, we show that classical suppressors defined by Horst (1941) are generated from causal structures which reveal the equivalence between suppressors and instrumental variables. Although the educational and psychological measurement literature has long recommended that researchers include suppressors in regression models, the causal inference literature has recently recommended that researchers exclude instrumental variables. The conflicting views between the two disciplines can be resolved by considering the different purposes of statistical models, prediction and causal explanation.

scholarly journals Comment on "Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach"

2021 ◽  
Author(s):  
Jarmo Mäkelä ◽  
Laila Melkas ◽  
Ivan Mammarella ◽  
Tuomo Nieminen ◽  
Suyog Chandramouli ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Prior Information ◽  
Causal Model ◽  
Concept Drift ◽  
Causal Structure ◽  
Causal Discovery ◽  
Causal Networks ◽  
Atmosphere Interaction ◽  
Model Structures ◽  
Discovery Algorithms

Abstract. This is a comment on "Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach" by Krich et al., Biogeosciences, 17, 1033–1061, 2020, which gives a good introduction to causal discovery, but confines the scope by investigating the outcome of a single algorithm. In this comment, we argue that the outputs of causal discovery algorithms should not usually be considered as end results but starting points and hypothesis for further study. We illustrate how not only different algorithms, but also different initial states and prior information of possible causal model structures, affect the outcome. We demonstrate how to incorporate expert domain knowledge with causal structure discovery and how to detect and take into account overfitting and concept drift.

The Acquisition and Use of Causal Structure Knowledge

2017 ◽  
Author(s):  
Benjamin Margolin Rottman
Keyword(s):  
Causal Structure ◽  
Causal Relations ◽  
How People Learn ◽  
Structure Knowledge ◽  
Normative Models ◽  
Causal Structures ◽  
Multiple Causes

This chapter provides an introduction to how humans learn and reason about multiple causal relations connected together in a causal structure. The first half of the chapter focuses on how people learn causal structures. The main topics involve learning from observations versus interventions, learning temporal versus atemporal causal structures, and learning the parameters of a causal structure including individual cause-effect strengths and how multiple causes combine to produce an effect. The second half of the chapter focuses on how individuals reason about the causal structure, such as making predictions about one variable given knowledge about other variables, once the structure has been learned. Some of the most important topics involve reasoning about observations versus interventions, how well people reason compared to normative models, and whether causal structure beliefs bias reasoning. In both sections the author highlights open empirical and theoretical questions.

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

scholarly journals The Inflation Technique Completely Solves the Causal Compatibility Problem

2020 ◽  
Vol 8 (1) ◽  
pp. 70-91 ◽  
Author(s):  
Miguel Navascués ◽  
Elie Wolfe
Keyword(s):  
Linear Programming ◽  
Latent Variables ◽  
Causal Explanation ◽  
Causal Structure ◽  
Categorical Variables ◽  
False Negatives ◽  
The Given ◽  
Causal Structures ◽  
Compatibility Question ◽  
Structure Graph

AbstractThe causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In 10.1515/jci-2017-0020, one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of linear programming. In this work, we develop a formal hierarchy of such causal compatibility relaxations. We prove that inflation is asymptotically tight, i.e., that the hierarchy converges to a zero-error test for causal compatibility. In this sense, the inflation technique fulfills a longstanding desideratum in the field of causal inference. We quantify the rate of convergence by showing that any distribution which passes the nth-order inflation test must be $\begin{array}{} \displaystyle {O}{\left(n^{{{-}{1}}/{2}}\right)} \end{array}$-close in Euclidean norm to some distribution genuinely compatible with the given causal structure. Furthermore, we show that for many causal structures, the (unrelaxed) causal compatibility problem is faithfully formulated already by either the first or second order inflation test.

scholarly journals Is coding a relevant metaphor for the brain?

2018 ◽  
Vol 42 ◽  
Author(s):  
Romain Brette
Keyword(s):  
Brain Function ◽  
Neural Coding ◽  
Causal Structure ◽  
Linear Chain ◽  
Neural Codes ◽  
Experimental Parameters ◽  
Representational Power ◽  
Causal Structures ◽  
Perceptual Systems ◽  
The Brain

Abstract “Neural coding” is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable (e.g., the spike count). Thus, the representational power of neural codes is much more limited than generally implied. Second, neural codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming an internal model. Neural codes are inadequate for this purpose because they are unstructured and therefore unable to represent relations. Third, coding variables are observables tied to the temporality of experiments, whereas spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular, and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a valid basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the representational requirements of cognition.

Causal discovery algorithms: A practical guide

2017 ◽  
Vol 13 (1) ◽  
pp. e12470 ◽  
Author(s):  
Daniel Malinsky ◽  
David Danks
Keyword(s):  
Causal Discovery ◽  
Practical Guide ◽  
Discovery Algorithms

scholarly journals The Impact of the Oil Sector on Commodity Prices: Correlation or Causation?

2010 ◽  
Vol 42 (3) ◽  
pp. 477-485 ◽  
Author(s):  
Sayed H. Saghaian
Keyword(s):  
Time Series ◽  
Graph Theory ◽  
Causal Structure ◽  
Commodity Prices ◽  
Oil Prices ◽  
Causal Link ◽  
Energy Markets ◽  
Oil Sector ◽  
The Impact ◽  
Causal Structures

The interconnections of agriculture and energy markets have increased through the rise in the new biofuel agribusinesses and the oil-ethanol-corn linkages. The question is whether these linkages have a causal structure by which oil prices affect commodity prices and through these links, instability is transferred from energy markets to already volatile agricultural markets. In this article, we present empirical results using contemporary time-series analysis and Granger causality supplemented by a directed graph theory modeling approach to identify the links and plausible contemporaneous causal structures among energy and commodity variables. The results show that although there is a strong correlation among oil and commodity prices, the evidence for a causal link from oil to commodity prices is mixed.

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