Causal Reasoning Research Articles

Differences in peri-urban and rural farm production decisions amid policy change in Nigeria

Enhancing productivity and profitability of farm households were key focuses among Nigerian agricultural policymakers in their design of the Agricultural Transformation Agenda (ATA) of 2011–2015. There were several prominent policy initiatives and market development activities included in the ATA. The main direct effects of implementing them were increased public expenditures on subsidized fertilizer and seeds and increased average productivity among subsidy recipients. There were also indirect effects pertaining to greater awareness of agricultural development initiatives among the public and increased farm input availability. However, due to relatively greater physical and transportation infrastructure in urban relative to rural areas, we hypothesized that farm households in peri-urban regions nearest to markets were better positioned to benefit from such initiatives as compared to rural farm households. In this context, the empirical analysis in this article estimates differences among peri-urban versus rural farm households regarding their crop produce marketing and farm input purchase decisions during the period of ATA policy implementation. The results support the hypothesis that peri-urban farm households increased purchases of farm inputs to a greater degree than did rural farm households as well as had higher crop sales values. Data limitations do not allow for determining the causal reason for relatively higher crop sales values. Overall, the policy and market development activities appear to have achieved some intended outcomes among farm households in both peri-urban and rural areas, but the impacts were most pronounced among households nearest to markets.

Defining and developing data literacy

Courses in data literacy, along with electronic tools to support them, have quickly sprung up for students from elementary through college levels, prompted in part by an increasingly apparent need for people of all ages to interpret data they encounter in popular media, as a requisite for responsible citizenship. What and how do students learn with the aid of such tools? As valuable as these may be in presenting data in varying transformable formats, they can be at most a beginning tool in a developmental progression toward data literacy that needs to be identified. Might at least the early phases of this development be accomplished as well or better without such tools? Data literacy does not emerge in one piece. What challenges do students encounter as they advance in a learning progression? Relevant to how they may do so is research on the development of higher-order inductive reasoning, in particular the coordination of theory and evidence in causal and explanatory reasoning. Possibly, very simple data displays provide young students all the complexity they need to address the initial conceptual challenges awaiting them.

Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering.

Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event temporality, causality, and dynamics spanning over the video. In this work, to address the task of event-level visual question answering, we propose a framework for cross-modal causal relational reasoning. In particular, a set of causal intervention operations is introduced to discover the underlying causal structures across visual and linguistic modalities. Our framework, named Cross-Modal Causal RelatIonal Reasoning (CMCIR), involves three modules: i) Causality-aware Visual-Linguistic Reasoning (CVLR) module for collaboratively disentangling the visual and linguistic spurious correlations via front-door and back-door causal interventions; ii) Spatial-Temporal Transformer (STT) module for capturing the fine-grained interactions between visual and linguistic semantics; iii) Visual-Linguistic Feature Fusion (VLFF) module for learning the global semantic-aware visual-linguistic representations adaptively. Extensive experiments on four event-level datasets demonstrate the superiority of our CMCIR in discovering visual-linguistic causal structures and achieving robust event-level visual question answering. The datasets, code, and models are available at https://github.com/HCPLab-SYSU/CMCIR.

Associative learning or Bayesian inference? Revisiting backwards blocking reasoning in adults

Causal reasoning is a fundamental cognitive ability that enables humans to learn about the complex interactions in the world around them. However, the cognitive mechanisms that underpin causal reasoning are not well understood. For instance, there is debate over whether Bayesian inference or associative learning best captures causal reasoning in human adults. The two experiments and computational models reported here were designed to examine whether adults engage in one form of causal inference called backwards blocking reasoning, whether the presence of potential distractors affects performance, and how adults' ratings align with the predictions of different computational models. The results revealed that adults engaged in backwards blocking reasoning regardless of whether distractor objects are present and that their causal judgements supported the predictions of a Bayesian model but not the predictions of two different associative learning models. Implications of these results are discussed.

A Logic for Desire Based on Causal Inference

Abstract Reasoning about desire plays a significant role in logic, artificial intelligence and philosophy, etc. In this paper, we propose an interpretation of desire that takes into account causal inference. To formalize this idea, we build a desire-causality model by combing the betterness model in preference logic and the causal model in the logic for causal reasoning. We then develop a logic for desire based on this semantics, and an axiomatization for our formal system is given.

Sufficient conditions for causal reasoning in mixtures epidemiology

Sufficient conditions for causal reasoning in mixtures epidemiology

MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny

BackgroundUnderstanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from.ResultsTo address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein–protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action.ConclusionsMAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN.

Corporate social responsibility and sustainability practices: mediating effect of green innovation and moderating effect of knowledge management in the manufacturing sector

PurposeThis study aims to examine the roles of corporate social responsibility (CSR), green innovation (GI) and knowledge management (KM) in boosting sustainable practices (SPs). It also investigates the mediating effect of green innovation and the moderating effect of KM in the relationship between CSR and SPs.Design/methodology/approachUsing measurement scales adapted from existing studies, a quantitative methodology with causal and deductive reasoning was used on data from an online survey with 322 respondents in manufacturing firms in the United Arab Emirates.FindingsThere was no direct significant positive relationship between CSR and SPs; the mediating role of GI was evident, whereas the moderating role of KM was not present.Research limitations/implicationsOnly manufacturing enterprises are considered in this study, with a single geographical case study highlighting CSR efforts.Practical implicationsThe findings reveal the positive influence of GI on CSR and SPs. This study contributes to the understanding of the influence of green processes and product innovation on applying social, environmental and sustainability practices to enhance the overall productivity, compliance, performance and well-being of the community.Originality/valueThis study collectively explores the causal relationships between these factors, besides offering new insights into the manufacturing industry in an emerging market.

Differential Diagnosis with Active Testing

The diagnosis of complex systems benefits greatly from a differential, multistep approach that narrows down the list of possible conditions or failures that share the same observable effects to a single root cause. We provide a suitable and practically applicable methodology for this. In extension to existing work, it covers all types of diagnostic actions, i.e., the observation of system properties, active testing and system interventions like providing a dedicated diagnostic input or forcing the system into discriminating states, but also the replacement of components. Combining all these possible steps into one probabilistic and causal reasoning framework, we I) stepwise generate the diagnostic model systematically to correctly cover the interplay of observations and diagnostic interventions, and II) provide decision support based on counterfactuals for the selection of the next diagnostic step, countering the vast number of possible actions that arise in machine diagnostic processes. We developed and successfully tried our methodology for diagnosing cyber-physical systems in the high-tech industry, but we found that it supports more processes, such as computing intervention actions for autonomous robots.

Validation of Bayesian design for broadband microslit panel absorbers using causal inference.

This paper discusses experimental validations of multilayer microslit panels (MSPs) designed via Bayesian inference to obtain both high sound absorption and wide bandwidth simultaneously. Microslit perforation in thin panels is similar to microperforated panels [Xiang, Fackler, Hou, and Schmitt (2022). J. Acoust. Soc. Am. 151(5), 3094-3103]. MSP absorbers in single-layer configurations are functioning in a limited frequency range. By stacking the MSPs in multiple layered structures, absorbing performance may be widened in frequency ranges while retaining high absorption coefficients. Besides design challenges of multiple MSPs in layered structures to fulfill a practical requirement and minimize fabrication complexity, this paper further discusses challenges in experimental validations when experimental results undesirably deviate from the initial Bayesian design. Causation analysis is applied to the validation efforts where a causal model-based inference effectively provides causal reasoning of fabrication inaccuracies. Along with the causal inference, a causal reasoning conducted in this work can guide corrections due to fabrication inaccuracies during the iterative validation process.

Native Ontological Framework Guides Causal Reasoning: Evidence from Wichi People

Abstract Causal cognition – how we perceive, represent and reason about causal events – are fundamental to the human mind, but it has rarely been approached in its cultural specificity. Here, we investigate this core concept among Wichi people, an indigenous group living in Chaco Forest. We focus on the Wichi, because their epistemological orientations and explanatory frameworks about ecosystem differ importantly from those documented among most Western majority-culture populations. We asked participants to reason about causes of events that involve the hunhat lheley (inhabitants of the earth: humans, non-human animals, plants and spiritual beings) and other entities of their ecosystem (e.g., lagoon). We find a native ontological framework that encompasses three interacting organizing principles. This new evidence highlights ways in which native categories guide causal reasoning. Our research challenge long-held assumptions that dichotomies – nature-culture or natural-supernatural – are universal features of the human mind.

Fictional placemaking creating meaningful contexts for causal reasoning in secondary school biology education

ABSTRACT In secondary science education, students often do not feel engaged with the scientific concepts that are taught, which hinders conceptual learning. This lack of engagement can be overcome by fictional placemaking. Therefore, the purpose of our design-based research is to explore how the creation and use of fictional places lead to meaningful contexts providing opportunities for the learning of biology. Four design principles were formulated from theories on: (1) pedagogy of place; (2) communities of practice; (3) imaginative teaching; and (4) story-based learning. Based on these principles a lesson series was designed in which students were challenged to create societies living in domes on Mars as a fictional place. This lesson series was conducted in a ninth-grade class (28 students) at pre-university level. It was evaluated on the contribution of the design principles to create a meaningful context for the learning of biology. This research focuses on causal reasoning, which is a key competency in biology. The analysis of artefacts of group work showed evidence that students expressed different types of causal reasoning. Reflection on each of the design principles made clear how fictional placemaking provides opportunities for the development of students’ causal reasoning.

When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles.

The aim of the present study was to identify eaters profiles using the latest advantages of Machine Learning approach to cluster analysis. A total of 317 participants completed an online-based survey including self-reported measures of body image dissatisfaction, bulimia, restraint, and intuitive eating. Analyses were conducted in two steps: (a) identifying an optimal number of clusters, and (b) validating the clustering model of eaters profile using a procedure inspired by the Causal Reasoning approach. This study reveals a 7-cluster model of eaters profiles. The characteristics, needs, and strengths of each eater profile are discussed along with the presentation of a continuum of eaters profiles. This conceptualization of eaters profiles could guide the direction of health education and treatment interventions targeting perceptual and eating dimensions.

One- and two-year-olds grasp that causes must precede their effects.

Knowing the temporal direction of causal relations is critical for producing desired outcomes and explaining events. Existing evidence suggests that children start to grasp that causes must precede their effects (the temporal priority principle) by age 3; however, whether younger children also understand this has, to our knowledge, not previously been tested. Given the importance of temporal priority in making sense of the world, we explored when knowledge of this principle develops. In the present study, conducted in a lab or museum in a Canadian city, 1- and 2-year-old children observed an adult perform action A on a puzzle box (e.g., spinning a dial), following which an effect E occurred (a sticker was dispensed), following which the adult performed action B (e.g., pushing a button; A-E-B sequence). In line with the temporal priority principle, toddlers were significantly more likely to manipulate A than B (Experiment 1, N = 41, 22 female), even when A was spatially disconnected from the sticker dispenser and further from it than action B (Experiment 2, N = 42, 25 female). In Experiment 3 (N = 50, 25 female), toddlers observed an A-B-E sequence such that both actions A and B were performed prior to effect E. Here, they primarily intervened on B, which ruled out that success in Experiments 1-2 was based on a primacy effect. A lack of any age effects across experiments suggests that within the second year of life, children already grasp that causes must precede their effects, providing key insights into causal reasoning in early childhood. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Probabilistic Modeling of Maritime Accident Scenarios Leveraging Bayesian Network Techniques

This paper introduces a scenario evolution model for maritime accidents, wherein Bayesian networks (BNs) were employed to predict the most probable causes of distinct types of maritime incidents. The BN nodes encompass factors such as accident type, life loss contingency, accident severity, quarter and time period of the accident, and type and gross tonnage of the involved ships. An analysis of 5660 global maritime accidents spanning the years 2005 to 2020 was conducted. Using Netica software, a tree augmented network (TAN) model was constructed, thus accounting for interdependencies among risk-influencing factors. To confirm these results, a validation process involving sensitivity analysis and historical accident records was performed. Following this, both forward causal inference and reverse diagnostic inference were carried out on each node variable to scrutinize the accident development trend and evolution process under preset conditions. The findings suggest that the model was competent in effectively predicting the likelihood of various accident scenarios under specific conditions, as well as extrapolating accident consequences. Forward causal reasoning unveiled that general cargo ships with a gross tonnage of 1–18,500 t were most prone to experiencing collision and stranding/grounding accidents in the first quarter. Reverse diagnostic reasoning indicated that, in the early morning hours, container ships, general cargo ships, and chemical ships with a tonnage of 1–18,500 t were less likely to involve life loss in the event of collision accidents.

Основные направления противодействия преступлениям коррупционной направленности в системе образования

Despite the measures taken to counteract corruption-related crimes, there is a positive trend in the activities of law enforcement agencies to identify, disclose, prosecute and prevent corruption as a negative social phenom-enon – the education system remains the most corrupt area. This type of criminal encroachments poses a threat to national security, sovereignty, democratic development, social justice, equality of rights of all citizens to receive free public education. Difficulties arising in the process of countering corruption-related crimes that hinder the successful implementation of preventive measures are due to the presence of selfish mutual inter-ests, both among students and employees of educational structures. The methodological basis of the research was private-scientific research methods: logical-legal, statistical and system analysis methods. The causal rea-son for the current situation is the insufficient effectiveness of control by law enforcement agencies, the low level of activity on the part of public associations, the imperfection of the system of protection of persons who have declared the facts of the existence of circumstances of conflicts of interest. Corruption crime in the educa-tion system is systemic and hidden. Corruption-related ties have been forming for a long time and include a wide range of participants both inside and outside the educational structure.

Sometimes It Is Better to Do Nothing: A New Argument for Causal Decision Theory

It is often thought that the main significant difference between evidential decision theory and causal decision theory is that they recommend different acts in Newcomb-style examples (broadly construed) where acts and states are correlated in peculiar ways. However, this paper presents a class of non-Newcombian examples that evidential decision theory cannot adequately model whereas causal decision theory can. Briefly, the examples involve situations where it is clearly best to perform an act that will not influence the desired outcome. On evidential decision theory—but not causal decision theory—this situation turns out to be impossible: acts that an agent does not think influence the desired outcome are never optimal. Typically, sophisticated versions of evidential decision theory emulate causal decision theoretic reasoning by (implicitly) conditioning on causal confounders, but in the kind of example considered here, this trick does not work. The upshot is that there is more to causal reasoning than has so far been appreciated.

Assessment of the predictability of inflow to reservoirs through Bayesian causality

ABSTRACT This research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a “proof of concept” that assesses the predictive capacity of the approach. The analytical power of CR revealed the logical temporal structure that defines the general behaviour of inflows, which was latent in the historical records. This allowed identifying/quantifying, through a dependence matrix, two temporal runoff fractions, one due to time and the other not. Finally, a predictive model for each temporal fraction was implemented, evaluating its forecasting skills through mean absolute error and root mean square error. This was applied to the reservoirs that supply water to the city of Ávila (Spain), whose watersheds present strong independent temporal behaviour. These results open new possibilities for developing predictive hydrological models within a CR modelling framework.

Effects of accelerated biological age on depressive symptoms in a causal reasoning framework

BackgroundDepression in middle-aged and elderly individuals is multifaceted and heterogeneous, linked to biological age (BA) based on aging-related biomarkers. However, due to confounding with chronological age and the absence of subgroup analysis and causal reasoning, the association between BA and depressive symptoms (DS) might be unstable and requires further investigation. MethodsWe utilized data from the China Health and Retirement Longitudinal Study (N = 9478) to perform association analysis, causal inference, and subgroup analysis. BA acceleration (BAA) was derived using machine learning and adjusted for chronological age. A generalized linear mixed-effects model (GLMM) tree algorithm was employed to identify subgroups. The causal reasoning frame included propensity score matching and fast large-scale almost matching exactly. ResultsIn the longitudinal analysis, BAA exhibited a consistent and significant positive association with DS, even after controlling for demographic characteristics, lifestyle factors, health status, and physical functions. This association remained unchanged within the causal framework. GLMM tree analysis identified three partitioning variables (sex, satisfaction, and BMI) and five subgroups. Further subgroup analysis revealed that BAA exerted the strongest effect on DS among women with less satisfying lives. LimitationsDepressive symptoms were evaluated through scale measurements rather than clinical diagnosis. The sample was derived from the general population, not the clinically depressed population. ConclusionsThis study provided the first longitudinal evidence that biological age acceleration increases depressive symptoms under causal reasoning and subgroup analysis, particularly among less satisfied women. And the association between BAA and DS was independent of known risk factors.

Local-to-Global Causal Reasoning for Cross-Document Relation Extraction

Cross-document relation extraction (RE), as an extension of information extraction, requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts. Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity. However, similarity-based methods cannot distinguish valid information from highly similar retrieved documents well. How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue. To address this problem, we design a novel local-to-global causal reasoning (LGCR) network for cross-document RE, which enables efficient distinguishing, filtering and global reasoning on complex information from a causal perspective. Specifically, we propose a local causal estimation algorithm to estimate the causal effect, which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE. Furthermore, based on the causal effect, we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning. Experimental results under the closed and the open settings of the large-scale dataset CodRED demonstrate our LGCR network significantly outperforms the state-of-the-art methods and validate the effectiveness of causal reasoning in confusing information processing.