Incremental Learning of Non-stationary Temporal Causal Networks for Telecommunication Domain
05 Dec 2017-pp 501-508
TL;DR: A novel framework is applied on a telecommunication operator’s data and the framework detects the concept drift related to changes in revenue associated with data usage and the incremental causal network learning algorithm updates the knowledge accordingly.
Abstract: In today’s competitive telecommunication industry understanding the causes that influence the revenue is of importance. In a continuously evolving business environment, the causes that influence the revenue keeps changing. To understand and quantify the effect of different factors we model it as a non-stationary temporal causal network. To handle the massive volume of data, we propose a novel framework as part of which we define rules to identify the concept drift and propose an incremental algorithm for learning non-stationary temporal causal structure from streaming data. We apply the framework on a telecommunication operator’s data and the framework detects the concept drift related to changes in revenue associated with data usage and the incremental causal network learning algorithm updates the knowledge accordingly.
References
More filters
TL;DR: A review of recent advances in causal inference can be found in this article, where a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a) is presented.
Abstract: This review presents empiricalresearcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be un- dertaken in moving from traditionalstatistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that un- derly all causal inferences, the languages used in formulating those assump- tions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coher- ent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interven- tions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attri- bution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both.
1,661 citations
02 Nov 2009
TL;DR: This article surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (including assessment of'regret', 'attribution' or 'causes of 'cause of 'cause' and 'cause of 'destruction' (also known as'mediation').
Abstract: This review presents empirical researchers with recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both.
960 citations
TL;DR: In this paper, the first step of the adjacency search of the PC-algorithm is replaced by several modifications that remove part or all of this order-dependence.
Abstract: We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al., 1993, 2000; Richardson, 1996; Colombo et al., 2012; Claassen et al., 2013). The first step of all these algorithms consists of the adjacency search of the PC-algorithm. The PC-algorithm is known to be order-dependent, in the sense that the output can depend on the order in which the variables are given. This order-dependence is a minor issue in low-dimensional settings. We show, however, that it can be very pronounced in high-dimensional settings, where it can lead to highly variable results. We propose several modifications of the PC-algorithm (and hence also of the other algorithms) that remove part or all of this order-dependence. All proposed modifications are consistent in high-dimensional settings under the same conditions as their original counterparts. We compare the PC-, FCI-, and RFCI-algorithms and their modifications in simulation studies and on a yeast gene expression data set. We show that our modifications yield similar performance in low-dimensional settings and improved performance in high-dimensional settings. All software is implemented in the R-package pcalg.
322 citations
Proceedings Article•
07 Dec 2009TL;DR: This paper proposes time-varying dynamic Bayesian networks (TV-DBN) for modeling the structurally varying directed dependency structures underlying non-stationary biological/neural time series and presents a kernel reweighted l1-regularized auto-regressive procedure which is the first practical and statistically sound method for structure learning of TV-DBNs.
Abstract: Directed graphical models such as Bayesian networks are a favored formalism for modeling the dependency structures in complex multivariate systems such as those encountered in biology and neural science. When a system is undergoing dynamic transformation, temporally rewiring networks are needed for capturing the dynamic causal influences between covariates. In this paper, we propose time-varying dynamic Bayesian networks (TV-DBN) for modeling the structurally varying directed dependency structures underlying non-stationary biological/neural time series. This is a challenging problem due the non-stationarity and sample scarcity of time series data. We present a kernel reweighted l1-regularized auto-regressive procedure for this problem which enjoys nice properties such as computational efficiency and provable asymptotic consistency. To our knowledge, this is the first practical and statistically sound method for structure learning of TV-DBNs. We applied TV-DBNs to time series measurements during yeast cell cycle and brain response to visual stimuli. In both cases, TV-DBNs reveal interesting dynamics underlying the respective biological systems.
184 citations
Proceedings Article•
25 Jul 2015TL;DR: It is shown that under appropriate assumptions, the causal structure is identifiable according to the formulated model, and a principled way for its estimation is proposed by extending Gaussian Process regression, which enables an automatic way to learn how the causal model changes over time.
Abstract: Most approaches to causal discovery assume a fixed (or time-invariant) causal model; however, in practical situations, especially in neuroscience and economics, causal relations might be time-dependent for various reasons. This paper aims to identify the time-dependent causal relations from observational data. We consider general formulations for time-varying causal modeling on stochastic processes, which can also capture the causal influence from a certain type of unobserved confounders. We focus on two issues: one is whether such a causal model, including the causal direction, is identifiable from observational data; the other is how to estimate such a model in a principled way. We show that under appropriate assumptions, the causal structure is identifiable according to our formulated model. We then propose a principled way for its estimation by extending Gaussian Process regression, which enables an automatic way to learn how the causal model changes over time. Experimental results on both artificial and real data demonstrate the practical usefulness of time-dependent causal modeling and the effectiveness of the proposed approach for estimation.
35 citations