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.

scholarly journals Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination

2017 ◽  
Author(s):  
Kun Zhang ◽  
Biwei Huang ◽  
Jiji Zhang ◽  
Clark Glymour ◽  
Bernhard Schölkopf
Keyword(s):  
Causal Model ◽  
Causal Structure ◽  
Heterogeneous Data ◽  
Causal Discovery ◽  
Data Sets ◽  
Real World Data ◽  
Experimental Conditions ◽  
Challenges And Opportunities ◽  
Changes Over Time ◽  
Orientation Determination

It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets (the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

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.

scholarly journals Scalable Probabilistic Causal Structure Discovery

2018 ◽  
Author(s):  
Dhanya Sridhar ◽  
Jay Pujara ◽  
Lise Getoor
Keyword(s):  
Domain Knowledge ◽  
Causal Structure ◽  
Scientific Discovery ◽  
Statistical Tests ◽  
Structural Constraints ◽  
Regulatory Interactions ◽  
Causal Networks ◽  
Soft Logic ◽  
Robustness To Noise ◽  
Synthetic Datasets

Complex causal networks underlie many real-world problems, from the regulatory interactions between genes to the environmental patterns used to understand climate change. Computational methods seek to infer these causal networks using observational data and domain knowledge. In this paper, we identify three key requirements for inferring the structure of causal networks for scientific discovery: (1) robustness to noise in observed measurements; (2) scalability to handle hundreds of variables; and (3) flexibility to encode domain knowledge and other structural constraints. We first formalize the problem of joint probabilistic causal structure discovery.  We develop an approach using probabilistic soft logic (PSL) that exploits multiple statistical tests, supports efficient optimization over hundreds of variables, and can easily incorporate structural constraints, including imperfect domain knowledge. We compare our method against multiple well-studied approaches on biological and synthetic datasets, showing improvements of up to 20% in F1-score over the best performing baseline in realistic settings.

scholarly journals A Ladder of Causal Distances

2021 ◽  
Author(s):  
Maxime Peyrard ◽  
Robert West
Keyword(s):  
Real World ◽  
Causal Model ◽  
Ground Truth ◽  
Causal Models ◽  
Causal Discovery ◽  
Major Significance ◽  
Benchmark Datasets ◽  
Real World Datasets ◽  
Discovery Algorithms ◽  
Graphical Structure

Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences. Evaluating the performance of causal discovery algorithms should ideally involve comparing the inferred models to ground-truth models available for benchmark datasets, which in turn requires a notion of distance between causal models. While such distances have been proposed previously, they are limited by focusing on graphical properties of the causal models being compared. Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. Pearl and Mackenzie [2018] have arranged the properties of causal models in a hierarchy called the ``ladder of causation'' spanning three rungs: observational, interventional, and counterfactual. Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder. Our definitions are intuitively appealing as well as efficient to compute approximately. We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets for which ground-truth causal models are available.

scholarly journals Invariant Causal Prediction for Nonlinear Models

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Christina Heinze-Deml ◽  
Jonas Peters ◽  
Nicolai Meinshausen
Keyword(s):  
Linear Models ◽  
Causal Model ◽  
Nonlinear Models ◽  
Causal Structure ◽  
Nonparametric Tests ◽  
Population Projections ◽  
World Population ◽  
Residual Distribution ◽  
Using Data

AbstractAn important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure. To this end, Invariant Causal Prediction (ICP) [1] has been proposed which learns a causal model exploiting the invariance of causal relations using data from different environments. When considering linear models, the implementation of ICP is relatively straightforward. However, the nonlinear case is more challenging due to the difficulty of performing nonparametric tests for conditional independence.In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables. We find that an approach which first fits a nonlinear model with data pooled over all environments and then tests for differences between the residual distributions across environments is quite robust across a large variety of simulation settings. We call this procedure “invariant residual distribution test”. In general, we observe that the performance of all approaches is critically dependent on the true (unknown) causal structure and it becomes challenging to achieve high power if the parental set includes more than two variables.As a real-world example, we consider fertility rate modeling which is central to world population projections. We explore predicting the effect of hypothetical interventions using the accepted models from nonlinear ICP. The results reaffirm the previously observed central causal role of child mortality rates.

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 Inflation Technique for Causal Inference with Latent Variables

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Elie Wolfe ◽  
Robert W. Spekkens ◽  
Tobias Fritz
Keyword(s):  
Probability Distribution ◽  
Causal Inference ◽  
Latent Variables ◽  
Causal Model ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Difficult Case ◽  
Common Causes ◽  
Multiple Copies ◽  
New Inequalities

AbstractThe problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the inflation technique for tackling this problem. An inflation of a causal structure is a new causal structure that can contain multiple copies of each of the original variables, but where the ancestry of each copy mirrors that of the original. To every distribution of the observed variables that is compatible with the original causal structure, we assign a family of marginal distributions on certain subsets of the copies that are compatible with the inflated causal structure. It follows that compatibility constraints for the inflation can be translated into compatibility constraints for the original causal structure. Even if the constraints at the level of inflation are weak, such as observable statistical independences implied by disjoint causal ancestry, the translated constraints can be strong. We apply this method to derive new inequalities whose violation by a distribution witnesses that distribution’s incompatibility with the causal structure (of which Bell inequalities and Pearl’s instrumental inequality are prominent examples). We describe an algorithm for deriving all such inequalities for the original causal structure that follow from ancestral independences in the inflation. For three observed binary variables with pairwise common causes, it yields inequalities that are stronger in at least some aspects than those obtainable by existing methods. We also describe an algorithm that derives a weaker set of inequalities but is more efficient. Finally, we discuss which inflations are such that the inequalities one obtains from them remain valid even for quantum (and post-quantum) generalizations of the notion of a causal model.

scholarly journals Causal Discovery Combining K2 with Brain Storm Optimization Algorithm

Molecules ◽  
2018 ◽  
Vol 23 (7) ◽  
pp. 1729
Author(s):  
Yinghan Hong ◽  
Zhifeng Hao ◽  
Guizhen Mai ◽  
Han Huang ◽  
Arun Kumar Sangaiah
Keyword(s):  
Real World ◽  
Data Science ◽  
Learning Algorithm ◽  
Causal Structure ◽  
Scientific Discovery ◽  
Causal Discovery ◽  
Causal Mechanism ◽  
Topological Order ◽  
Brain Storm Optimization ◽  
Real World Datasets

Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods.

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