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Aims: The aims of this study were to estimate potentially clinically important drug–drug interaction (DDI) prevalence, and the average causal effect of DDI exposure on adverse drug reaction (ADR)‐related hospital admission, and to examine differences in health‐related quality of life (HRQoL) and length of stay (LOS) per DDI exposure in an older (≥65 years) population acutely hospitalized. Methods: This was a cross‐sectional study conducted among 798 older individuals acutely admitted to hospital in Ireland between 2016 and 2017. Medication (current/recently discontinued/over‐the‐counter) and clinical data (e.g., creatinine clearance) were available. DDIs were identified using the British National Formulary (BNF) and Stockley's Drug Interactions. Causal inference models for DDI exposure on ADR‐related hospital admission were developed using directed acyclic graphs. Multivariable logistic regression was used to estimate the average causal effect. Differences in HRQoL (EQ‐5D) and LOS per DDI exposure were examined non‐parametrically. DDI prevalence, adjusted odds ratios (aOR), and 95% confidence intervals (CIs) are reported. Results: A total of 782 (98.0%) individuals using two or more drugs were included. Mean age was 80.9 (SD ± 7.5) years (range: 66–105); 52.2% were female; and 45.1% (n = 353) had an ADR‐related admission. At admission, 316 (40.4% [95% CI: 37.0–43.9]) patients had at least one DDI. The average causal effect of DDI exposure on ADR‐related hospital admission was aOR = 1.21 [95% CI: 0.89–1.64]. This was significantly increased by exposure to: DDIs which increase bleeding risk (aOR = 2.00 [1.26–3.12]); aspirin‐warfarin (aOR = 2.78 [1.37–5.65]); and esomeprazole‐escitalopram (aOR = 3.22 [1.13–10.25]. DDI‐exposed patients had lower HRQoL (mean EQ‐5D = 0.49 [±0.39]) compared those non‐DDI‐exposed (mean EQ‐5D = 0.57 [±0.41]), (P =.03); and greater median LOS in hospital (8 [IQR5–16]days) compared those non‐DDI‐exposed (7 [IQR 4–14] days),(P =.04). Conclusions: Potentially clinically important DDIs carry an increased average causal effect on ADR‐related admission, significantly (two‐fold) by exposure to DDIs that increase bleeding risk, which should be targeted for medicine optimization. [ABSTRACT FROM AUTHOR]

Introduction: Software to predict the impact of aging on physical appearance is increasingly popular. But it does not consider the complex interplay of factors that contribute to skin aging. Objectives: To predict the +15‐year progression of clinical signs of skin aging by developing Causal Bayesian Belief Networks (CBBNs) using expert knowledge from dermatologists. Material and methods: Structures and conditional probability distributions were elicited worldwide from dermatologists with experience of at least 15 years in aesthetics. CBBN models were built for all phototypes and for ages ranging from 18 to 65 years, focusing on wrinkles, pigmentary heterogeneity and facial ptosis. Models were also evaluated by a group of independent dermatologists ensuring the quality of prediction of the cumulative effects of extrinsic and intrinsic skin aging factors, especially the distribution of scores for clinical signs 15 years after the initial assessment. Results: For easiness, only models on African skins are presented in this paper. The forehead wrinkle evolution model has been detailed. Specific atlas and extrinsic factors of facial aging were used for this skin type. But the prediction method has been validated for all phototypes, and for all clinical signs of facial aging. Conclusion: This method proposes a skin aging model that predicts the aging process for each clinical sign, considering endogenous and exogenous factors. It simulates aging curves according to lifestyle. It can be used as a preventive tool and could be coupled with a generative AI algorithm to visualize aging and, potentially, other skin conditions, using appropriate images. [ABSTRACT FROM AUTHOR]

Smart homes are Cyber-Physical Systems (CPS) where multiple devices and controllers cooperate to achieve high-level goals. Causal knowledge on relations between system entities is essential for enabling system selfadaption to dynamic changes. As house configurations are diverse, this knowledge is difficult to obtain. In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows. Starting with considering all possible relations, we progressively discarded non-correlated variables. Next, we identified causal relations from the remaining correlations by employing "do-operations." The obtained CBN could then be employed for causal inference. The main challenges of this approach included "non-doable variables" and limited scalability. To address these issues, we propose three extensions: (i) early pruning weakly correlated relations to reduce the number of required do-operations, (ii) introducing aggregate variables that summarize relations between weakly coupled sub-systems, and (iii) applying the method a second time to perform indirect do interventions and handle non-doable relations. We illustrate and evaluate the efficiency of these contributions via examples from the smart home and power grid domain. Our proposal leads to a decrease in the number of operations required to learn the CBN and in an increased accuracy of the learned CBN, paving the way toward applications in large CPS. [ABSTRACT FROM AUTHOR]

Falls stand for a prevalent problem among the elderly and a significant public health concern. In recent years, a growing number of apps have been developed to assist in terms of the delivery of more effective and efficient falls prevention programs. All of these apps rely on a massive elderly personal database gathered from hospitals, mutual health groups, and other organizations that help the elderly. Information on an older adult is constantly changing, and it may become obsolete at any time, contradicting what we currently know about the same person. As a result, it needs to be checked and updated on a regular basis in order to maintain database consistency and hence provide a better service. This research work describes an Obsolete Personal Information Update System (OIUS) developed as part of the elderly-fall prevention project. Our OIUS intends to control and update the information gathered about each older adult in real-time, to provide consistent information on demand, and to provide tailored interventions to carers and fall-risk patients. The method discussed here is based upon a polynomial-time algorithm built on top of a causal Bayesian network that models the older adults data. The outcome is presented as an AND-OR recommendation Tree with a certain level of accuracy. On an aged personal information base, we perform an empirical study for such a model. Experiments corroborate our OIUS's viability and effectiveness. [ABSTRACT FROM AUTHOR]

In real world contexts of reasoning about evidence, that evi-dence frequently arrives sequentially. Moreover, we often can-not anticipate in advance what kinds of evidence we will even-tually encounter. This raises the question of what we do to ourexisting models when we encounter new variables to consider.The standard normative framework for probabilistic reasoningyields the same ultimate outcome whether multiple pieces ofevidence are acquired in sequence or all at once, and it is in-sensitive to the order in which that evidence is acquired. Thisequivalence, however, holds only if all potential evidence isincorporated in a single model from the outset. Hence little isknown about what happens when evidence sets are expandedincrementally. Here, we examine this contrast formally and re-port the results of the first study, to date, that examines howpeople navigate such expansions.

In causal Bayesian networks (CBNs),it is a fundamental problem to compute the causal effect of sum product.From the perspective of a directed acyclic graph,we show every CBN has a corresponding Bayesian network.Intervention is a fundamental operation in CBNs.Similar to Bayesian networks,CBNs also have the pruning strategy.After pruning the barren nodes,this paper devises an optimized jointree algorithm to compute the full atomic intervention on each node in a CBN.Then,this paper explores the multiple interventions on multiple nodes,and finds that multiple interventions have the commutative property.On the basis of the commutative property in multiple interventions,this paper proves the strategies,which can be used to optimize the computation of the causal effect of multiple interventions.Finally,we report experimental results to demonstrate the efficiency of our algorithm to compute the causal effects in CBNs.

Recent research suggests that people do not perform wellon some of the most crucial components of causal reason-ing: probabilistic independence, diagnostic reasoning, and ex-plaining away. Despite this, it remains unclear what con-texts would affect people’s reasoning in these domains. Inthe present study we investigated the influence of manipulatingpriors of causes and structural complexity of Causal BayesianNetworks (CBNs) on the above components. Overall we foundthat participants largely accepted the priors and understoodprobabilistic independence, but engaged in inaccurate diagnos-tic reasoning and insufficient explaining away behavior. More-over, the effect of manipulating priors on participants’ perfor-mance in diagnostic reasoning and explaining away was sig-nificantly larger in a structurally less complex CBN than in astructurally more complex CBN.

This paper has two main parts. In the first part, we motivate a kind of indeterminate, suppositional credences by discussing the prospect for a subjective interpretation of a causal Bayesian network (CBN), an important tool for causal reasoning in artificial intelligence. A CBN consists of a causal graph and a collection of interventional probabilities. The subjective interpretation in question would take the causal graph in a CBN to represent the causal structure that is believed by an agent, and interventional probabilities in a CBN to represent suppositional credences. We review a difficulty noted in the literature with such an interpretation, and suggest that a natural way to address the challenge is to go for a generalization of CBN that allows indeterminate credences. In the second part, we develop a decision-theoretic foundation for such indeterminate suppositional credences, by generalizing a theory of coherent choice functions to accommodate some form of act-state dependence. The upshot is a decision-theoretic framework that is not only rich enough to, so to speak, ground the probabilities in a subjectively interpreted causal network, but also interesting in its own right, in that it accommodates both act-state dependence and imprecise probabilities. [ABSTRACT FROM AUTHOR]

We develop a quantum probability model that can account forsituations where people’s causal judgments violate theproperties of causal Bayes nets and demonstrate how theparameters of our model can be interpreted to provideinformation about underlying cognitive processes. Weimplement this model within a hierarchical Bayesianinference framework that allows us to systematically identifyindividual differences and also provide a latent classificationof individuals into categories of causal and associativereasoners. Finally, we implement a basic normative causalBayes net within the same inference framework that allows usto directly compare quantum and classical probability modelsusing Bayes factors

Using a machine learning method, this study aimed to identify unique causal networks of genes associated with bone, brain, and lung metastasis of breast cancer. Bayesian network analysis identified differentially expressed genes in primary breast cancer tissues, in bone, brain, and lung breast cancer metastatic tissues, and the clinicopathological features of patients obtained from the Gene Expression Omnibus microarray datasets. We evaluated the causal Bayesian networks of breast metastasis to distant sites (bone, brain, or lung) by (i) measuring how well the structures of each specific type of breast cancer metastasis fit the data, (ii) comparing the structures with known experimental evidence, and (iii) reporting predictive capabilities of the structures. We report for the first time that the molecular gene signatures are specific to the different types of breast cancer metastasis. Several genes, including CHPF, ARC, ANGPTL4, NR2E1, SH2D1A, CTSW, POLR2J4, SPTLC1, ILK, ALDH3B1, PDE6A, SCTR, ADM, HEY1, KCNF1, and UVRAG, were found to be predictors of the risk for site-specific metastasis of breast cancer. Expression of POLR2JA, SPTLC1, ILK, ALDH3B1, and the estrogen receptor was significantly associated with breast cancer bone metastasis. Expression of PDE6A and NR2E1 was causally linked to breast cancer brain metastasis. Expression of HEY1, KCNF1, UVRAG, and the estrogen and progesterone receptors was strongly associated with breast cancer lung metastasis. The causal Bayesian network structures of these genes identify potential interactions among the genes in distant metastases of breast cancer, including to the bone, brain, and lung, and may serve as target candidates for treatment of breast cancer metastasis. [ABSTRACT FROM AUTHOR]

The study of people’s ability to engage in causal probabilistic reasoning has typically used fixed-point estimates for key figures. For example, in the classic taxi-cab problem, where a witness provides evidence on which of two cab companies (the more common ‘green’/less common ‘blue’) were responsible for a hit and run incident, solvers are told the witness’s ability to judge cab color is 80%. In reality, there is likely to be some uncertainty around this estimate (perhaps we tested the witness and they were correct 4/5 times), known as second-order uncertainty, producing a distribution rather than a fixed probability. While generally more closely matching real world reasoning, a further important ramification of this is that our best estimate of the witness’ accuracy can and should change when the witness makes the claim that the cab was blue. We present a Bayesian Network model of this problem, and show that, while the witness’s report does increase our probability of the cab being blue, it simultaneously decreases our estimate of their future accuracy (because blue cabs are less common). We presented this version of the problem to 131 participants, requiring them to update their estimates of both the probability the cab involved was blue, as well as the witness’s accuracy, after they claim it was blue. We also required participants to explain their reasoning process and provided follow up questions to probe various aspects of their reasoning. While some participants responded normatively, the majority self-reported ‘assuming’ one of the probabilities was a certainty. Around a quarter assumed the cab was green, and thus the witness was wrong, decreasing their estimate of their accuracy. Another quarter assumed the witness was correct and actually increased their estimate of their accuracy, showing a circular logic similar to that seen in the confirmation bias/belief polarization literature. Around half of participants refused to make any change, with convergent evidence suggesting that these participants do not see the relevance of the witness’s report to their accuracy before we know for certain whether they are correct or incorrect.

We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal relationships among model variables. To this effect, we explore the use of two types of independencies—context-specific independence (CSI) and mutual independence (MI). We use CSI to identify the candidate set of causal relationships and then use MI to quantify their strengths and construct a causal model. We validate the learned models on benchmark networks and demonstrate the effectiveness when compared to some of the state-of-the-art Causal Bayesian Network Learning algorithms from observational Data.

The study of people's ability to engage in causal probabilistic reasoning has typically used fixed-point estimates for key figures. For example, in the classic taxi-cab problem, where a witness provides evidence on which of two cab companies (the more common 'green'/less common 'blue') were responsible for a hit and run incident, solvers are told the witness's ability to judge cab color is 80%. In reality, there is likely to be some uncertainty around this estimate (perhaps we tested the witness and they were correct 4/5 times), known as second-order uncertainty, producing a distribution rather than a fixed probability. While generally more closely matching real world reasoning, a further important ramification of this is that our best estimate of the witness' accuracy can and should change when the witness makes the claim that the cab was blue. We present a Bayesian Network model of this problem, and show that, while the witness's report does increase our probability of the cab being blue, it simultaneously decreases our estimate of their future accuracy (because blue cabs are less common). We presented this version of the problem to 131 participants, requiring them to update their estimates of both the probability the cab involved was blue, as well as the witness's accuracy, after they claim it was blue. We also required participants to explain their reasoning process and provided follow up questions to probe various aspects of their reasoning. While some participants responded normatively, the majority self-reported 'assuming' one of the probabilities was a certainty. Around a quarter assumed the cab was green, and thus the witness was wrong, decreasing their estimate of their accuracy. Another quarter assumed the witness was correct and actually increased their estimate of their accuracy, showing a circular logic similar to that seen in the confirmation bias/belief polarization literature. Around half of participants refused to make any change, with convergent evidence suggesting that these participants do not see the relevance of the witness's report to their accuracy before we know for certain whether they are correct or incorrect. [ABSTRACT FROM AUTHOR]

Many theoretical and empirical contributions to the Predictive Processing account emphasize the important role of precision modulation of prediction errors. Recently it has been proposed that the causal models used in human predictive processing are best formally modeled by categorical probability distributions. Crucially, such distributions assume a well-defined, discrete state space. In this paper we explore the consequences of this formalization. In particular we argue that the level of detail of generative models and predictions modulates prediction error. We show that both increasing the level of detail of the generative models and decreasing the level of detail of the predictions can be suitable mechanisms for lowering prediction errors. Both increase precision, yet come at the price of lowering the amount of information that can be gained by correct predictions. Our theoretical result establishes a key open empirical question to address: How does the brain optimize the trade-off between high precision and information gain when making its predictions? [ABSTRACT FROM AUTHOR]

Within the last years, probabilistic causality has become a very active research topic in artificial intelligence and statistics communities. Due to its high impact in various applications involving reasoning tasks, machine learning researchers have proposed a number of techniques to learn Causal Bayesian Networks. Within the existing works in this direction, few studies have explicitly considered the role that decisional guidance might play to alternate between observational and experimental data processing. In this paper, we go further by introducing a serendipitous strategy to elucidate semantic background knowledge provided by the domain ontology to learn the causal structure of Bayesian Networks. We also complement our contribution with an enrichment process by which it will be possible to reuse these causal discoveries, support the evolving character of the semantic background and make an ontology evolution. Finally, the proposed method will be validated through simulations and real data analysis. [ABSTRACT FROM AUTHOR]

It is a nontrivial task to build an accurate emerging pattern (EP) classifier from high-dimensional data because we inevitably face two challenges 1) how to efficiently extract a minimal set of strongly predictive EPs from an explosive number of candidate patterns, and 2) how to handle the highly sensitive choice of the minimal support threshold. To address these two challenges, we bridge causal relevance and EP discriminability (the predictive ability of emerging patterns) to facilitate EP mining and propose a new framework of mining EPs from high-dimensional data. In this framework, we study the relationships between causal relevance in a causal Bayesian network and EP discriminability in EP mining, and then reduce the pattern space of EP mining to direct causes and direct effects, or the Markov blanket (MB) of the class attribute in a causal Bayesian network. The proposed framework is instantiated by two EPs-based classifiers, CE-EP and MB-EP, where CE stands for direct Causes and direct Effects, and MB for Markov Blanket. Extensive experiments on a broad range of data sets validate the effectiveness of the CE-EP and MB-EP classifiers against other well-established methods, in terms of predictive accuracy, pattern numbers, running time, and sensitivity analysis. [ABSTRACT FROM PUBLISHER]

Thanks to their different senses, human observers acquire multiple information coming from their environment. Complex cross-modal interactions occur during this perceptual process. This article proposes a framework to analyze and model these interactions through a rigorous and systematic data-driven process. This requires considering the general relationships between the physical events or factors involved in the process, not only in quantitative terms, but also in term of the influence of one factor on another. We use tools from information theory and probabilistic reasoning to derive relationships between the random variables of interest, where the central notion is that of conditional independence. Using mutual information analysis to guide the model elicitation process, a probabilistic causal model encoded as a Bayesian network is obtained. We exemplify the method by using data collected in an audio-visual localization task for human subjects, and we show that it yields a well-motivated model with good predictive ability. The model elicitation process offers new prospects for the investigation of the cognitive mechanisms of multisensory perception. [ABSTRACT FROM AUTHOR]

We define a model of learning probabilistic acyclic circuits using value injection queries, in which fixed values are assigned to an arbitrary subset of the wires and the value on the single output wire is observed. We adapt the approach of using test paths from the Circuit Builder algorithm (Angluin et al., 2009) to show that there is a polynomial time algorithm that uses value injection queries to learn acyclic Boolean probabilistic circuits of constant fan-in and log depth. We establish upper and lower bounds on the attenuation factor for general and transitively reduced Boolean probabilistic circuits of test paths versus general experiments. We give computational evidence that a polynomial time learning algorithm using general value injection experiments may not do much better than one using test paths. For probabilistic circuits with alphabets of size three or greater, we show that the test path lemmas (Angluin et al., 2009, 2008b) fail utterly. To overcome this obstacle, we introduce function injection queries, in which the values on a wire may be mapped to other values rather than just to themselves or constants, and prove a generalized test path lemma for this case. [ABSTRACT FROM AUTHOR]

Using microarray experiments, we can model causal relationships of genes measured through mRNA expression levels. To this end, it is desirable to compare experiments of the system under complete interventions of some genes, such as by knock out of some genes, with experiments of the system under no interventions. However, it is expensive and difficult to conduct wet lab experiments of complete interventions of genes in a biological system. Thus, it will be helpful if we can discover promising causal relationships among genes with no interventions or incomplete interventions, such as by applying a treatment that has unknown effects to modeled genes, in order to identify promising genes to perturb in the system that can later be verified in wet laboratories. In this paper we use causal Bayesian networks to implement a causal discovery algorithm—the equivalence local implicit latent variable scoring method (EquLIM)—that identifies promising causal relationships even with a small dataset generated from no or incomplete interventions. We then apply EquLIM to analyze the five-gene-network data and compare EquLIM's predictions with true causal pairwise relationships between the genes. [ABSTRACT FROM AUTHOR]

We address the problem of improving the reliability of independence-based causal discovery algorithms that results from the execution of statistical independence tests on small data sets, which typically have low reliability. We model the problem as a knowledge base containing a set of independence facts that are related through Pearl's well-known axioms. Statistical tests on finite data sets may result in errors in these tests and inconsistencies in the knowledge base. We resolve these inconsistencies through the use of an instance of the class of defeasible logics called argumentation, augmented with a preference function, that is used to reason about and possibly correct errors in these tests. This results in a more robust conditional independence test, called an argumentative independence test. Our experimental evaluation shows clear positive improvements in the accuracy of argumentative over purely statistical tests. We also demonstrate significant improvements on the accuracy of causal structure discovery from the outcomes of independence tests both on sampled data from randomly generated causal models and on real-world data sets. [ABSTRACT FROM AUTHOR]

Abstract: The main topic of this paper is evaluating a system that uses the expected value of experimentation for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knock-out experiment) and observations (e.g., passively observing the expression level of a “wild-type” gene). We introduce a system called GEEVE (causal discovery in Gene Expression data using Expected Value of Experimentation), which implements expected value of experimentation in discovering causal pathways using gene expression data. GEEVE provides the following assistance, which is intended to help biologists in their quest to discover gene-regulation pathways: [•] Recommending which experiments to perform (with a focus on “knock-out” experiments) using an expected value of experimentation (EVE) method. [•] Recommending the number of measurements (observational and experimental) to include in the experimental design, again using an EVE method. [•] Providing a Bayesian analysis that combines prior knowledge with the results of recent microarray experimental results to derive posterior probabilities of gene regulation relationships. In recommending which experiments to perform (and how many times to repeat them) the EVE approach considers the biologist’s preferences for which genes to focus the discovery process. Also, since exact EVE calculations are exponential in time, GEEVE incorporates approximation methods. GEEVE is able to combine data from knock-out experiments with data from wild-type experiments to suggest additional experiments to perform and then to analyze the results of those microarray experimental results. It models the possibility that unmeasured (latent) variables may be responsible for some of the statistical associations among the expression levels of the genes under study. To evaluate the GEEVE system, we used a gene expression simulator to generate data from specified models of gene regulation. Using the simulator, we evaluated the GEEVE system using a randomized control study that involved 10 biologists, some of whom used GEEVE and some of whom did not. The results show that biologists who used GEEVE reached correct causal assessments about gene regulation more often than did those biologists who did not use GEEVE. The GEEVE users also reached their assessments in a more cost-effective manner. [Copyright &y& Elsevier]

The main topic of this paper is modeling the expected value of experimentation (EVE) for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knockout experiment) and observations (e.g., passively observing the expression level of a “wild-type” gene). We introduce a system called GEEVE (causal discovery in Gene Expression data using Expected Value of Experimentation), which implements expected value of experimentation in discovering causal pathways using gene expression data. GEEVE provides the following assistance, which is intended to help biologists in their quest to discover gene-regulation pathways:In recommending which experiments to perform (and how many times to repeat them) the EVE approach considers the biologist’s preferences for which genes to focus the discovery process. Also, since exact EVE calculations are exponential in time, GEEVE incorporates approximation methods. GEEVE is able to combine data from knockout experiments with data from wild-type experiments to suggest additional experiments to perform and then to analyze the results of those microarray experimental results. It models the possibility that unmeasured (latent) variables may be responsible for some of the statistical associations among the expression levels of the genes under study.To evaluate the GEEVE system, we used a gene expression simulator to generate data from specified models of gene regulation. The results show that the GEEVE system gives better results than two recently published approaches (1) in learning the generating models of gene regulation and (2) in recommending experiments to perform. [Copyright &y& Elsevier]

Causal judgements in explaining-away situations, where multiple independent causes compete to account for a common effect, are ubiquitous in both everyday and specialised contexts. Despite their ubiquity, cognitive psychologists still struggle to understand how people reason in these contexts. Empirical studies have repeatedly found that people tend to 'insufficiently' explain away: that is, when one cause explains the presence of an effect, people do not sufficiently reduce the probability of other competing causes. However, the diverse accounts that researchers have proposed to explain this insufficiency suggest we are yet to find a compelling account of these results. In the current research we explored the novel possibility that insufficiency in explaining away is driven by: (i) some people interpreting probabilities as propensities, i.e. as tendencies of a physical system to produce an outcome and (ii) some people splitting the probability space among the causes in diagnostic reasoning, i.e. by following a strategy we call 'the diagnostic split'. We tested these two hypotheses by manipulating (a) the characteristics of cover stories to reflect different degrees to which the propensity interpretation of probability was pronounced, and (b) the prior probabilities of the causes which entailed different normative amounts of explaining away. Our results were in line with the extant literature as we found insufficient explaining away. However, we also found empirical support for our two hypotheses, suggesting that they are a driving force behind the reported insufficiency. [ABSTRACT FROM AUTHOR]

We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal relationships among model variables. To this effect, we explore the use of two types of independencies-context-specific independence (CSI) and mutual independence (MI). We use CSI to identify the candidate set of causal relationships and then use MI to quantify their strengths and construct a causal model. We validate the learned models on benchmark networks and demonstrate the effectiveness when compared to some of the state-of-the-art Causal Bayesian Network Learning algorithms from observational Data., (Copyright © 2020 Ramanan and Natarajan.)

Bayesian network (BN) structure learning algorithms are almost always designed to recover the structure that models the relationships that are shared by the instances in a population . While accurately learning such population-wide Bayesian networks is useful, learning Bayesian networks that are specific to each instance is often important as well. For example, to understand and treat a patient (instance), it is critical to understand the specific causal mechanisms that are operating in that particular patient. We introduce an instance-specific BN structure learning method that searches the space of Bayesian networks to build a model that is specific to an instance by guiding the search based on attributes of the given instance (e.g., patient symptoms, signs, lab results, and genotype). The structure discovery performance of the proposed method is compared to an existing state-of-the-art BN structure learning method, namely an implementation of the Greedy Equivalence Search algorithm called FGES, using both simulated and real data. The results show that the proposed method improves the precision of the model structure that is output, when compared to GES, especially for those variables that exhibit context-specific independence.