Causal Reasoning Research Articles

Assembly-Oriented Task Sequence Planning for a Dual-Arm Robot

One of the goals for the deployment of dual-arm robots is to imitate and replace human workers to complete manufacturing tasks. Moreover, especially in scenarios where workpieces are randomly scattered on the workbench, endowing a robotic system with a capacity of scheduling task sequences will undoubtedly expand its application prospects and improve the autonomy. For that purpose, this paper proposes a dual-arm robot task sequence planning approach based on environmental constraints and causal reasoning among tasks. In this work, the Monte Carlo method, the Gaussian Mixture Model and the binary functions are adopted to evaluate the constraints from the robots. Meanwhile, constraints from workpieces are addressed with a geometric method combined with its semantic relations. In addition to the environmental constraints, which consist of constraints from the robot and workpieces, the causal relations among tasks are considered. Finally, all the above information is exploited to construct a graphical structure for task sequence planning, where workpieces are regarded as vertices and their semantic relations are edges with attributes. The effectiveness of the approach is demonstrated using various simulation experiments within different scene layouts rather than using several specified tasks.

"What was meant to be" versus "what might have been": Effects of culture and control on counterfactual thinking.

Counterfactual thinking is a ubiquitous feature of daily life with links to causal reasoning. Therefore, we argue that cultures that vary in perceptions of what controls important life outcomes may also vary in counterfactual thought. Investigating White American and United Arab Emirates-based Arab participants' counterfactual potency and spontaneous counterfactual thinking, we found that Arab participants endorsed counterfactual thoughts less than White Americans, and were unaffected by the routine nature of action when negative outcomes were severe. Differences in counterfactual endorsement in response to severe negative outcomes were linked to greater beliefs in divine control and fate in Arab participants, and not to religiosity, reinforcing an important role of perceptions of control in counterfactual thought. However, although reporting less counterfactual endorsement overall, Arabs showed a similar pattern of counterfactual thought to White Americans when negative outcomes were mild, or when reporting spontaneous thought. Arabs likewise showed a similar pattern of regret as White Americans regardless of event severity, reporting more regret when outcomes resulted from unusual action. These patterns suggest a dissociation between affect and cognition, and between what kind of outcomes are subject to counterfactual scrutiny in Arab participants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A New Explainable Deep Learning Framework for Cyber Threat Discovery in Industrial IoT Networks

Industrial Internet of Things (IIoT) and Industry 4.0 empower interrelation among manufacturing processes, industrial machines, and utility services. The time-critical data collected from heterogeneous sensing devices are usually communicated to processing points for analysis and aggregation as the basis of IIoT. The IIoTs’ service quality typically depends on data integrity and accuracy, which could be exploited by injecting malicious events, such as false data injection and data poisoning attacks. Thus, effective anomaly recognition and explanation are critical for ensuring quality services and empowering security administrators to interpret the causal reasoning of prediction decisions and underlying data evidence. This study proposes an autoencoder-based detection framework using convolutional and recurrent networks to discover cyber threats in IIoT networks and explain the model. A two-step sliding window (SW) is applied to learn the latent representations of data features better. Malicious points from the raw time series are transformed into fixed-length series through the first-step SW. Every series is converted into continuous-time-reliant subseries via another smaller SW to learn latent representations of malicious events. Fully connected networks use the extracted temporal and spatial features for the classification and explanation of attack events. The empirical results revealed that this framework effectively extracts features that include contexts of malicious patterns. This demonstrated that the proposed framework is robust in detecting malicious events using multiple evaluation metrics and outperforming the contemporary state-of-the-art methods, indicating its suitability as an operative application method in real-world IIoT-based networks.

Precursory Prevention: Togetherness for Better Health

Precursory Prevention: Togetherness for Better Health

Scientific and Folk Theories of Viral Transmission: A Comparison of COVID-19 and the Common Cold.

Disease transmission is a fruitful domain in which to examine how scientific and folk theories interrelate, given laypeople’s access to multiple sources of information to explain events of personal significance. The current paper reports an in-depth survey of U.S. adults’ (N = 238) causal reasoning about two viral illnesses: a novel, deadly disease that has massively disrupted everyone’s lives (COVID-19), and a familiar, innocuous disease that has essentially no serious consequences (the common cold). Participants received a series of closed-ended and open-ended questions probing their reasoning about disease transmission, with a focus on causal mechanisms underlying disease contraction, transmission, treatment, and prevention; non-visible (internal) biological processes; and ontological frameworks regarding what kinds of entities viruses are. We also assessed participants’ attitudes, such as their trust in scientific experts and willingness to be vaccinated. Results indicated complexity in people’s reasoning, consistent with the co-existence of multiple explanatory frameworks. An understanding of viral transmission and viral replication existed alongside folk theories, placeholder beliefs, and lack of differentiation between viral and non-viral disease. For example, roughly 40% of participants who explained illness in terms of the transmission of viruses also endorsed a non-viral folk theory, such as exposure to cold weather or special foods as curative. Additionally, participants made use of competing modes of construal (biological, mechanical, and psychological) when explaining how viruses operate, such as framing the immune system response (biological) as cells trying to fight off the virus (psychological). Indeed, participants who displayed greater knowledge about viral transmission were significantly more likely to anthropomorphize bodily processes. Although comparisons of COVID-19 and the common cold revealed relatively few differences, the latter, more familiar disease elicited consistently lower levels of accuracy and greater reliance on folk theories. Moreover, for COVID-19 in particular, accuracy positively correlated with attitudes (trusting medical scientists and taking the disease more seriously), self-protective behaviors (such as social distancing and mask-wearing), and willingness to be vaccinated. For both diseases, self-assessed knowledge about the disease negatively predicted accuracy. The results are discussed in relation to challenges for formal models of explanatory reasoning.

Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations

Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have shown significant predictive performance compared with traditional models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasonings are more critical in learning science. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful features: individual skill mastery, ability profile (learning transfer across skills) and problem difficulty by using data mining techniques. IKT’s prediction of future student performance is made using a Tree Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning based student models. IKT also shows better student performance prediction than deep learning based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.

Reasoning about Causal Models with Infinitely Many Variables

Generalized structural equations models (GSEMs) (Peters and Halpern 2021), are, as the name suggests, a generalization of structural equations models (SEMs). They can deal with (among other things) infinitely many variables with infinite ranges, which is critical for capturing dynamical systems. We provide a sound and complete axiomatization of causal reasoning in GSEMs that is an extension of the sound and complete axiomatization provided by Halpern (2000) for SEMs. Considering GSEMs helps clarify what properties Halpern's axioms capture.

Healing fields foundation: the challenge faced in “effectually” covering the proverbial last mile

Study level/applicability This case study can be used at the graduate and executive levels. Subject area This case study can be used in entrepreneurship, leadership, social entrepreneurship and human resource management. Case overview Healing Fields Foundation is a non-governmental organisation (NGO) that was co-founded by Mukti Bosco to create an affordable and quality health-care ecosystem, primarily through women. The pragmatism of Mukti and her strong alignment with the core values of the foundation ensured that they emerged unscathed from past challenges. During the second wave of the pandemic in 2021, they employed bikers on a contractual basis to satisfy last-mile delivery demand in rural India. However, owing to the recovery post the second wave, the demand for their services dropped and subsequently their earnings. Being provided with four options by her COO, Mukti is cognisant of the social implications her decisions will have on all the stakeholders in the ecosystem. Expected learning outcomes A. Identify and prioritise key stakeholders of the organisation for effective decision-making. B. Differentiate effectual from causal reasoning and apply their right balance while making decisions. C. Delineate social entrepreneurs from their for-profit, non-mission-driven counterparts. D. Create value for the organisation’s stakeholders through the management of its diverse workforce. E. Formulate entrepreneurial solutions through the application of relevant concepts and frameworks. Subject code CSS 3: Entrepreneurship.

Scene graph generation with award-punishment strategy

The scene graph can provide a structured representation for downstream tasks given an image. To generate a fine-grained one, many researchers have attempted to alleviate the long-tailed dataset bias by energy-based learning, causal reasoning or other mechanisms. Nevertheless, they are still restricted to the biased dataset because of the redundancy of head information and the lack of tail information. In this work, we propose a comprehensive and ground-breaking model called Balanced Award-Punishment Model (BAPM) to tackle the problem. The BAPM consists of the stochastic strategy module (SSM), the knowledge transfer module (KTM) and the lateral inhibition loss (LIL). Concretely, the SSM takes dropout to form two different domain spaces for KTM, enhancing the tail information of the object level by constantly learning from another domain. The KTM aims to acquire abundant knowledge of tail predicates by transferring the fine-grained information from one domain to another. The SSM and KTM can be regarded as knowledge awards (KA) due to the incentive for tail data. The LIL mimics the competitive mechanism of neurons to smoothly adjust the weight of each object or each predicate by focal strategy. We regard the LIL as redundancy punishment (RP) owing to taking the restriction for head data into account. Under the joint award-punishment scheme, our approach has achieved state-of-the-art performance on two complementary metrics. The quantitative and qualitative experimental results on Visual Genome dataset show that our BAPM further reduce the dataset bias and generates more diverse scene graphs.

Primary and Secondary School Teachers’ Perception of the Assessment of Historical Knowledge and Skills Based on Classroom Activities and Exercises

The aim of this paper is to identify the most suitable activities and exercises for the development of historical knowledge and skills and their subsequent evaluation. On this basis, a quantitative study has been carried out among primary and secondary school teachers in several Spanish regions in which their perception of what types of exercises and activities (of those proposed in their History or Social Science classes) are most suitable for assessing the historical knowledge and skills that students are expected to acquire has been extracted. The results show that exercises involving the interpretation of texts and images which require students to think about and apply the historical knowledge acquired are very useful, as are questions which seek historical explanation and causal reasoning; however, objective tests (multiple-choice tests, linking dates with events, etc.) or short questions about historical events or characters are not considered to be very suitable for the adequate development of historical knowledge and, therefore, of historical competences. Broadly speaking, it is recommended that this line of research be continued so that other authors can replicate these findings, deepening their knowledge of these instruments, whose didactic commitment could serve as an argument in the face of the excessive time pressure faced by today’s teachers.

Quantifying performance of passive systems in an integrated small modular reactor under uncertainties using multilevel flow modelling and stochastic collocation method

Many newly designed advanced nuclear reactors deploy several passive systems to increase the inherent safety. However, uncertainties often exist in these new designs because of lacking sufficient operation experience and experimental data, which leads to the possible deviation on the performance of systems from their prospective values and increases the risk of the plant. Thus, methods should be developed to quantify the influence of uncertainties on the performance of the passive systems properly to improve our understanding of these new designs and provide support for decision-making.Traditionally, the system performance analysis under uncertainties is performed by Monte-Carlo based methods (MCS), which is extremely time-consuming. Thus, to circumvent the limitation of traditional Monte-Carlo based method, a novel hybrid method that combines multilevel flow modelling and stochastic collocation (MFM-SC) is proposed in this paper. The MFM-SC method utilizes the MFM to model the functions of collaborative working passive systems and inference the relevant uncertain parameters according to causal reasoning rules. Then, the stochastic collocation method which combines the strength of Monte Carlo methods and stochastic Galerkin methods is used to create an orthogonal polynomial based surrogate model for the thermal hydraulic model. With the efficiency of the surrogate model in computation, the quantification of the performance of passive systems under uncertainties which requires thousands of repetitive computations can be easily conducted.An example test case is also presented in this paper to analyze the performance of core makeup tanks and the steam drum in an integrated small modular reactor during a station blackout accident under multiple uncertainties using MFM-SC method. The statistical results of the deviation range of the systems’ performance under uncertainties are given according to thousands of experiments using a surrogate model, and the response surfaces are also calculated based on the MFM-SC method to reveal the relation between the uncertain inputs and the systems output separately. The result shows the significant improvement in the computational efficiency of the suggested MFM-SC method, and it can provide essential information for decision-making.

Unlimited Associative Learning as a Null Hypothesis

AbstractA common strategy in comparative cognition is to require that one reject associative learning as an explanation for behavior before concluding that an organism is capable of causal reasoning. In this paper, I argue that standard causal-reasoning tasks can be explained by a powerful form of associative learning: unlimited associative learning (UAL). The lesson, however, is not that researchers should conduct more studies to reject UAL, but that they should instead focus on 1) enriching the cognitive hypothesis space and 2) testing a broader range of information processing patterns—errors, biases and limits, rather than successful problem solving alone.

Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning

In discrete manufacturing, the knowledge about causal relationships makes it possible to avoid unforeseen production downtimes by identifying their root causes. Learning causal structures from real-world settings remains challenging due to high-dimensional data, a mix of discrete and continuous variables, and requirements for preprocessing log data under the causal perspective. In our work, we address these challenges proposing a process for causal reasoning based on raw machine log data from production monitoring. Within this process, we define a set of transformation rules to extract independent and identically distributed observations. Further, we incorporate a variable selection step to handle high-dimensionality and a discretization step to include continuous variables. We enrich a commonly used causal structure learning algorithm with domain-related orientation rules, which provides a basis for causal reasoning. We demonstrate the process on a real-world dataset from a globally operating precision mechanical engineering company. The dataset contains over 40 million log data entries from production monitoring of a single machine. In this context, we determine the causal structures embedded in operational processes. Further, we examine causal effects to support machine operators in avoiding unforeseen production stops, i.e., by detaining machine operators from drawing false conclusions on impacting factors of unforeseen production stops based on experience.

IS CAUSAL REASONING HARDER THAN PROBABILISTIC REASONING?

AbstractMany tasks in statistical and causal inference can be construed as problems of entailment in a suitable formal language. We ask whether those problems are more difficult, from a computational perspective, for causal probabilistic languages than for pure probabilistic (or “associational”) languages. Despite several senses in which causal reasoning is indeed more complex—both expressively and inferentially—we show that causal entailment (or satisfiability) problems can be systematically and robustly reduced to purely probabilistic problems. Thus there is no jump in computational complexity. Along the way we answer several open problems concerning the complexity of well-known probability logics, in particular demonstrating the ${\exists \mathbb {R}}$ -completeness of a polynomial probability calculus, as well as a seemingly much simpler system, the logic of comparative conditional probability.

Causal reasoning without mechanism.

Unobservable mechanisms that tie causes to their effects generate observable events. How can one make inferences about hidden causal structures? This paper introduces the domain-matching heuristic to explain how humans perform causal reasoning when lacking mechanistic knowledge. We posit that people reduce the otherwise vast space of possible causal relations by focusing only on the likeliest ones. When thinking about a cause, people tend to think about possible effects that participate in the same domain, and vice versa. To explore the specific domains that people use, we asked people to cluster artifacts. The analyses revealed three commonly employed mechanism domains: the mechanical, chemical, and electromagnetic. Using these domains, we tested the domain-matching heuristic by testing adults' and children's causal attribution, prediction, judgment, and subjective understanding. We found that people's responses conform with domain-matching. These results provide evidence for a heuristic that explains how people engage in causal reasoning without directly appealing to mechanistic or probabilistic knowledge.

To What Extent Is General Intelligence Relevant to Causal Reasoning? A Developmental Study.

To what extent general intelligence mechanisms are associated with causal thinking is unclear. There has been little work done experimentally to determine which developing cognitive capacities help to integrate causal knowledge into explicit systems. To investigate this neglected aspect of development, 138 children aged 5–11 studying at mainstream primary schools completed a battery of three intelligence tests: one investigating verbal ability (WASI vocabulary), another looking at verbal analogical (Verbal Analogies subset of the WRIT), and a third assessing non-verbal/fluid reasoning (WASI block design). Children were also interviewed over the course of three causal tasks (sinking, absorption, and solution), with the results showing that the developmental paths exhibited uneven profiles across the three causal phenomena. Children consistently found that explaining solution, where substances disappeared toward the end of the process, was more challenging. The confirmatory factor analyses suggested that the impact of cognitive ability factor in explicitly identifying causal relations was large. The proportion of the direct effect of general intelligence was 66% and it subsumed the variances of both verbal measures. Of this, 37% was the indirect effect of age. Fluid reasoning explained a further 28% of the variance, playing a unique role in causal explanation. The results suggested that, overall, cognitive abilities are substantially related to causal reasoning, but not entirely due to developmental differences in “g” during the age periods studied.

Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

The global spread of the coronavirus disease 2019 (COVID-19) pandemic has affected the world in many ways. Due to the communicable nature of the disease, it is difficult to investigate the causal reason for the epidemic’s spread sufficiently. This study comprehensively investigates the causal relationship between the spread of COVID-19 and mobility level on a multi time-scale and its influencing factors, by using ensemble empirical mode decomposition (EEMD) and the causal decomposition approach. Linear regression analysis investigates the significance and importance of the influential factors on the intrastate and interstate causal strength. The results of an EEMD analysis indicate that the mid-term and long-term domain portrays the macroscopic component of the states’ mobility level and COVID-19 cases, which represents overall intrinsic characteristics. In particular, the mobility level is highly associated with the long-term variations of COVID-19 cases rather than short-term variations. Intrastate causality analysis identifies the significant effects of median age and political orientation on the causal strength at a specific time-scale, and some of them cannot be identified from the existing method. Interstate causality results show a negative association with the interstate distance and the positive one with the airline traffic in the long-term domain. Clustering analysis confirms that the states with the higher the gross domestic product and the more politically democratic tend to more adhere to social distancing. The findings of this study can provide practical implications to the policymakers that whether the social distancing policies are effectively working or not should be monitored by long-term trends of COVID-19 cases rather than short-term.

Performance assessment of Bayesian Causal Modelling for runoff temporal behaviour through a novel stability framework

A strong innovative tendency is nowadays emerging that largely comprises new hydrological modelling approaches, based on Causal Reasoning through Probabilistic Graphical Modelling (PGM), because its ability to support probabilistic reasoning from data with uncertainty. These novel modelling frameworks are quite diverse and disperse not only in terms of techniques but also regarding its aims. It seems necessary to find a general and robust methodology for assessing its performance. This paper aims to provide a novel general methodology for assessing the performance of PGM based on Bayesian Causality for modelling and analysing the riverś runoff behaviour. For it, a structured four-step approach is developed and showed throughout the paper. The proposed methodology begins with the identification of the two main factors that condition the Bayesian Causal (BC) Modelling: the number of synthetic series (data amount) and the number of intervals for probability distributions for training and learning processes. The developed analysis comprises the definition of three levels for the first factor and seven levels for the second one, as well as the design of an innovative stability framework that assesses the level of BC Modelling performance. Furthermore, it has been necessary to create-define two novel indexes, named “Similarity and Stability Indexes” from 21 scenarios arising from the combination of the levels of the both factors. The optimal combination of factors is identified through a bi-objectives recursive approach based on previous indexes. Main results drawn successfully show a high relationship between the level of modelling performance, measured in terms of stability, and the river runoff temporal behaviour, measured in terms of temporal dependence. This research may help water managers and engineers to develop more rigorous and robust hydrological causal modelling implementations.

From Personal Observations to Recommendation of Tailored Interventions based on Causal Reasoning: a case study of Falls Prevention in Elderly Patients

ObjectiveWhile the challenge of estimating the efficacy of therapies using observational data has received a lot of attention, little work has been done on estimating the treatment effect from interventions. In this paper, we tackle this problem by proposing an early guidance system based on a causal Bayesian network (CBN) for recommending personalized interventions. We are interested in the elderly fall prevention context. The objective is to develop a practical tool to help doctors estimate the effects of each intervention (or compound interventions) on a given patient and then choose the one that best fits each patient’s health situation to reduce the risk of falling. MethodsOn a real-world elderly information base, we undertake an empirical investigation for the proposed approach, which is based on a 44-node CBN. Then, we describe what is possible to achieve using state-of-the-art machine learning methods, namely Support Virtual Machine (SVM), Decision Tree (DT), and Bayesian Network (BN), and how well these methods can be used in recommending personalized interventions compared to the proposed approach. Results1174 elderly patients from Lille University Hospital, between January 2005 and December 2018 are included. The results reveal that none of the classifiers is significantly superior to the others, even if BN delivers somewhat better results (41.6%) and DT most often slightly lower performance (31.2%). Results also show that none of these three classifiers performs comparable to the proposed system (89.7%). The interventions recommended by the system are in agreement with the expert’s judgment to a satisfactory level. The reaction of the physicians to the proposed system in its first trial version was very favorable. ConclusionThe study showed the efficacy and utility of the causality-based strategy in recommending tailored interventions to prevent elderly falls compared to automated learning methods that had failed to infer a solid interventional paradigm for precision medicine.

Observing interventions: a logic for thinking about experiments

AbstractThis paper makes a first step towards a logic of learning from experiments. For this, we investigate formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment (Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge; Woodward, 2003, Making Things Happen, vol. 114 of Oxford Studies in the Philosophy of Science. Oxford University Press). In a first step we extend a causal model (Briggs, 2012, Philosophical Studies, 160, 139–166; Galles and Pearl, 1998, An axiomatic characterisation of causal counterfactuals. Foundations of Science, 3, 151–182; Halpern, 2000, Axiomatizing causal reasoning. Journal of Artificial Intelligence Research, 12, 317–337; Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge) with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk about the knowledge of an agent and information update. The resulting logic can model reasoning about thought experiments. However, it is unable to account for learning from experiments, which is clearly brought out by the fact that it validates the principle of no learning for interventions. Therefore, in a second step, we implement a more complex notion of knowledge (Nozick, 1981, Philosophical Explanations. Harvard University Press, Cambridge, Massachusetts) that allows an agent to observe (measure) certain variables when an experiment is carried out. This extended system does allow for learning from experiments. For all the proposed logics, we provide a sound and complete axiomatization.