Quantitative probing: Validating causal models with quantitative domain knowledge

: We describe research fusing heterogeneous information in an effort eventually to detect terrorists, reduce false alarms, and exonerate those falsely identified. The specific research is more humble, using synthetic data and first versions of fusion methods. Both the information and the fusion methods are subject to deep uncertainty. The information may also be fragmentary, indirect, soft, conflicting, and even deceptive. We developed a research prototype of an analyst centric fusion platform. This uses (1) causal computational models rooted in social science to relate observable information about individuals to an estimate of the threat that the individual poses and (2) a battery of different methods to fuse across information reports. We account for uncertainties about the causal model, the information, and the fusion methods. We address structural and parametric uncertainties, including uncertainties about the uncertainties, at different levels of detail. We use a combination of (1) probabilistic and parametric methods, (2)alternative models, and (3) alternative fusion methods that include nonlinear algebraic combination, Bayesian inference, and an entropy-maximizing approach. This paper focuses primarily on dealing with deep uncertainty in multiple dimensions.

Chapter 1 Causal Models: The Representational Infrastructure for Moral Judgment

Cross-sectional associations between voice and psychological factors are known, but changes over time offer opportunities to refine our understanding of their interactions and consider customized treatment options. Study objectives were to measure relationships between voice and psychological factors using ecological momentary assessment and applying (1) group-level time series analysis and (2) group and (3) individual causal modeling to identify key psychological factors relevant for voice outcomes. Adults (N = 32) with primary muscle tension dysphonia completed multiple assessments daily for 10 days. Measures included items from the Voice Handicap Index-10, voice-adapted perceived present control scale, items from NIH PROMIS and the NIH Toolkit to assess distress, and the Positive and Negative Affect Scale. Group-level time series analysis was conducted using dynamic structural equation modeling; causal analysis utilized the Greedy Fast Causal Inference algorithm. In group-level time series analyses, neither perceived control nor distress predicted subsequent timepoint voice handicap scores. In group-level causal modeling, anxiety was causal for voice handicap, but perceived control was not. Individual-level analyses identified various causal factors for voice handicap including perceived control and negative affect, and to a lesser extent, serenity, anxiety, somatic arousal, and stress. Group-level analyses may obscure important heterogeneity that is identifiable using individual-level causal analyses. For example, perceived control was not identified as predictive or causal for voice handicap at the group level; but was a salient causal factor for voice handicap in some individuals. Causal modeling using intensive longitudinal datasets offers a potential avenue for individualized treatment approaches.

Background: People with psychosis have a reduced life expectancy by 15 years, mainly due to preventable physical illnesses for which obesity is a precursor. Obesity is three times more common in psychosis and antipsychotics are an important cause. Prediction could individualise obesity treatment but current models are not fully actionable for individuals.Aims: We test whether antipsychotic-induced weight increase at 1 year is causally mediated by weight change in the first 12 weeks of treatment, rather than the following 40 weeks. We then develop and internally validate a causal actionable prediction pathway to prevent antipsychotic-induced obesity.Methods: We use parallel causal mediation analysis to determine the natural direct and indirect, and total effects of antipsychotic choice on weight in a trial of olanzapine versus haloperidol in 97 participants. We develop a baseline causal actionable prediction model to predict weight gain at 12 weeks in 172 participants, and a 12-week model to predict obesity at 1 year in 97 of the participants, and then demonstrate counterfactual prediction.Results: Antipsychotic-induced weight gain at 1 year appears to be causally mediated by weight change during the first 12 weeks treatment – indirect effect 5.70 (95% CI 2.83 to 8.66). At internal validation, the discrimination c-statistic for the baseline causal actionable prediction model was 0.732 and the calibration slope was 0.768. For the 12-week model, the c-statistic was 0.914 and the calibration slope was 0.601. We use the models to predict the counterfactual outcomes of antipsychotic choice and 12-week weight change.Conclusions: Our study shows it may be early, rather than later weight change, which causally mediates antipsychotic-induced weight gain at 1 year. We also demonstrate the potential for causal prediction of counterfactuals targeting obesity for true precision medicine. Further research in larger samples is necessary before causal prediction models could be applied clinically.

The increasing prevalence of multiple, long time series data fuels the hope for gaining better understanding and more precise prediction of various phenomena in many domains, hence calling for the development of new methodologies in time series analysis. Two features pertinent to the practical usefulness of any time series model are sparsity and causality. A sparse time series model means that only a small subset of model parameters are non-zero, which is usually preferable to its non-sparse counterparts in terms of interpretability, robustness, parsimony and estimation variability. Causality refers to the dependence structure for which only past and present information is used. Causality is an indispensable feature for certain purposes such as prediction, and is usually desirable when modeling real-world phenomena. This dissertation devotes to the development of two time series methodologies that incorporate sparsity and causality. In the first part of the thesis, we introduce the sparse causal dynamic linear regression model. Two approaches are proposed for construction of such models. The first one utilizes the frequency domain solution of a classic two-sided dynamic linear model and augments it with a simple soft-thresholding operator. This approach is computationally efficient and enjoys good performance theoretically and empirically under the assumption that the true dynamic regression relationship is causal and sparse. The second approach constructs a sparse causal dynamic linear model by minimizing certain penalized mean squared prediction error. This approach is computationally intensive but is superior to the first approach as it does not require the causal assumption. It enjoys better empirical performance and opens up meaningful extensions. In the second part of the thesis, we introduce the sparse causal dynamic principal component analysis. This procedure also has two variants. The first one computes the principal component series as a sparse and causal linear filter of the original data. The filter is obtained by minimizing a penalized reconstruction loss functional in the frequency domain. The principal component series so obtained enjoys good performance in terms of summarizing high-dimensional data, estimating factors in dynamic factor models, and prediction. However, it is computationally intensive. The second approach imposes sparsity structure on the innovation representation of the data generating process, which leads to simpler computational algorithms. In addition, this method is of interest in its own right for certain applications in finance and econometrics.

Understanding the interplay of environmental and anthropogenic drivers is essential to interpret past changes and anticipate future dynamics of marine fish stocks. Using Norwegian spring-spawning herring (NSSH) as a case study, we applied an iterative structured causal modelling framework combining causal knowledge analysis and inference—using structural equation models—to quantify the respective effects of climate and fisheries on stock biomass and catches. Initial simple models, grounded in prior ecological understanding, yielded implausible estimates when confronted with observational data, underscoring the necessity to treat data structure and content as integral to model design. We gradually incorporated multiple causal pathways, feedback loops, and confounders. Final models explained over 90% of observed variation in both biomass and catch and showed strong support for the roles of recruitment, prior biomass, and total allowable catch (TAC) advice. We found no causal effect of ocean climate variables (Relative Heat Content and Arctic Water Content) on year-to-year changes in NSSH biomass, nor of Spawning Stock Biomass on catches. Instead, catch levels were primarily governed by TAC advice and the degree of agreement between fishing nations. We conclude that reliable causal inference in ecological systems requires iterative model refinement, causal knowledge analysis, and explicit accounting for data structure. Adopting and transparently reporting this approach would benefit ecological studies used to support fisheries management.

Aims: Accurately identifying diagnostic biomarkers for Autism Spectrum Disorder (ASD) is crucial for enabling early diagnosis and timely intervention. Brain causal networks, which outline causal relationships and information transmission pathways among different brain regions, hold significant diagnostic value for ASD. Identifying ASD-relevant causal relationships between brain regions is critical for ASD diagnosis and for elucidating its pathophysiology. This study proposes a new model called the Peter and Clark Momentary Conditional Independence (PCMCI)-Support Vector Machine (SVM) model, which can identify diagnostic biomarkers for ASD. Methods: The brain blood oxygen level-dependent signals of 116 brain regions from 167 participants, consisting of 72 participants with ASD and 95 Typical Controls (TCs), were used to derive brain causal networks by detecting inter-regional causal relationships using five causal discovery algorithms; the PCMCI algorithm, spectral dynamic causal modeling, Granger causal analysis, Transfer Entropy, and Liang-Kleeman information flow causal analysis. Then, the brain causal relationships were fed into the SVM, Random Forest (RF), and K-Nearest Neighbors (KNN) classifiers, respectively to classify individuals with ASD and TCs, and thereby obtain a model suitable for identifying diagnostic biomarkers for ASD through causal network analysis. Results: Experimental results demonstrate that the PCMCI-SVM model achieves an accuracy of 91.29% in ASD identification and outperforms the other four causal discovery algorithms, as well as the RF and KNN classifiers. Moreover, our analysis indicates that the right thalamus and right middle temporal gyrus are potential diagnostic biomarkers for ASD. Additionally, the causal relationship between [left inferior parietal→right insula] was found to be associated with the dorsal attention network, while [right cuneus→left cuneus] was associated with the visual network, suggesting that disruptions in these causal relationships may impair the functional integrity of their respective subnetworks. Conclusion: Our findings show that cognitive processes and brain connectivity are largely influenced by causal interactions between different brain regions. These potential diagnostic biomarkers not only offer insights into the neurofunctional mechanisms of ASD but also hold promise for improving diagnostic accuracy for ASD.

As discussed in Chapter , the analytical approach requires a detailed quantitative mathematical model in order to be effective. For large-scale systems, such information may not be available or may be too costly and timeconsuming to obtain. An alternative method for process monitoring is to use knowledge-based methods such as causal analysis, expert systems, and pattern recognition. These techniques are based on qualitative models, which can be obtained through causal modeling of the system, expert knowledge, a detailed description of the system, or fault-symptom examples. Causal analysis techniques are based on the causal modeling of fault-symptom relationships. Qualitative and semi-quantitative relationships in these causal models can be obtained without using first principles. Causal analysis techniques including signed directed graphs and symptom trees are primarily used for diagnosing faults. These techniques are described in Section 12.2.

Abstract: The economic effects of defense spending on growth in South Korea is not clear cut. Previous studies have reported mixed findings. Since export has been the main engine of South Korean economic development, I have systematically examined the indirect relationship between defense spending and economic growth via export, employing a Box-Jenkins ARIMA (Autoregressive Integrated Moving Average) causal model. The results of the analysis reveal that an increase in defense expenditures has an immediate dampening effect on export. Thus, reductions in defense spending may lead to an increase in export, which in turn stimulates economic growth. In other words, South Korea may expect some “peace dividend.”

Vehicle lane changing is one of the most important topics in autonomous driving and is the behavior most likely to cause vehicle accidents. In order to optimize the performance of vehicle lane change by controlling the speed of the vehicle, which makes the lane change safer, more efficient, and more eco-friendly, a causal inference-based speed control framework is proposed in this study. First, variables affecting the lane change efficiency are selected and their treatment effects are obtained by conducting causal effect analysis and causal refutation analysis. Second, the conditional average treatment effect is estimated by taking the safety and eco-friendly requirements into consideration and applying the double machine learning version causal forest model. Finally, a controller is built, and grid search is used to optimize lane change decision-making. By conducting numerical experiments, the average lane change duration decreased by 5.9% and average velocity increased from 7.165 m/s to 7.791 m/s. The results show that the framework has good performance on improving the efficiency and environmental friendliness of a lane change process while ensuring safety. The proposed framework provides a new approach to speed control for discretionary lane-changing behavior. The approach can be well adapted to a vehicle-to-vehicle communication environment and is expected to be applied to vehicle-assisted driving systems in the future.

Intracellular free Ca2+ in hepatocytes regulates a wide range of hepatic functions. Whole tissue Ca2+ imaging studies show wave‐like spatial patterns of cytosolic Ca2+ in response to circulating hormonal stimuli in liver lobules. Ca2+ waves elicited by hormones such as vasopressin are rooted in cell‐autonomous GPCR activation and the downstream canonical signaling cascade. Spatial Ca2+ waves are caused by gap junction‐mediated molecule exchange between adjacent hepatocytes. Ca2+ waves override cellular heterogeneity with regards to intracellular Ca2+ dynamics leading to a coordinated response to stimuli.Although a wealth of knowledge exists on intracellular Ca2+ dynamics, a quantitative description of lobular scale Ca2+ waves is lacking. We acquired data on vasopressin induced Ca2+ transients imaged in a 2D optical slice in perfused rat liver for 1300 hepatocytes across several lobules. We used a combination of causal time series modeling and model‐based analysis to identify what factors contribute towards propagation of Ca2+ waves in liver lobules.Time series modeling of our data suggested that although Ca2+ waves appeared to traverse entire lobules, only a subset of hepatocytes residing in any lobule formed causally connected “islands”. Within these islands, hepatocytes were causally connected to up to six neighbors. However information flow between adjacent hepatocytes within these islands was not aligned in a unidirectional fashion, as required for wave‐like propagation. Visualization of the Ca2+ profiles within an island revealed existence of cell‐autonomous Ca2+ transients superimposed on spatially propagating waves.We next analyzed our data using a receptor oriented, ODE‐based computational model of Ca2+ wave propagation. Our model suggested that Ca2+ waves originated from extended regions near the pericentral lobular zone and propagated towards the periportal region. Ca2+ waves in our model resulted from parameter gradients and IP3 exchange. Our analysis also revealed that Ca2+ wave propagation was robust to some non‐interacting hepatocytes due to high degree of causal connections for a given hepatocyte.Our analyses revealed that robust lobular scale Ca2+ waves arise due spatial gradients of intracellular signaling components and IP3 exchange between a hepatocyte and several of its neighbors residing within the same island. The existence of multidirectional causal edges as seen in our causal analysis could arise due to cell‐autonomous Ca2+ response of hepatocytes within a small region which do not propagate beyond a few cells. Although we modeled intercellular interactions as gap junction mediated IP3 exchange, additional phenomena such as paracrine signaling as well as cellular arrangement in 3D could contribute towards coupling of hepatocyte Ca2+ responses across sinusoids. These aspects must be included to improve our description of spatial Ca2+ wave dynamics in liver lobules.Support or Funding InformationNIH NIAAA: R01 AA018873This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

The success of a research project, as determined by its perceived impact, is important for its ability to attract, mobilise, and manage funding, which constitutes a key indicator of the sustainability and relevance of the activities undertaken. The success of project teams involved in research and development processes is also significantly influenced by factors associated with human resources and is consolidated over time through their implementation in effective collaboration and management practices. In this context, the proposed study investigates the causal interdependencies and models the cause-and-effect relationships among 28 factors, with a focus on human resource factors that impact the success of research and development projects. The study aims to identify a series of particularities that differentiate research and development projects from other types of projects. The findings contribute to the specialised literature by empirically validating the interdependencies between human resource factors and offering an interesting perspective for managers, helping them to focus their efforts on the variables with the greatest potential to influence performance. Furthermore, the findings also contribute to the identification of a series of particularities that differentiate research and development projects from projects in the industrial or financial-banking sectors, particularities that impact the way activities are planned and managed and the establishment of the criteria used to intelligently direct managers’ efforts.

Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causal discovery usually relies on expert validation or prior analysis to improve the reliability of recovered causality, which is yet limited by the scarcity of expert resources. Recently, Large Language Models (LLM) have been used for causal analysis across various domain-specific scenarios, suggesting its potential as autonomous expert roles in guiding data-based structure learning. However, integrating LLMs into causal discovery faces challenges due to inaccuracies in LLM-based reasoning on revealing the actual causal structure. To address this challenge, we propose an error-tolerant LLM-driven causal discovery framework. The error-tolerant mechanism is designed three-fold with sufficient consideration on potential inaccuracies. In the LLM-based reasoning process, an accuracy-oriented prompting strategy restricts causal analysis to a reliable range. Next, a knowledge-to-structure transition aligns LLM-derived causal statements with structural causal interactions. In the structure learning process, the goodness-of-fit to data and adherence to LLM-derived priors are balanced to further address prior inaccuracies. Evaluation of eight real-world causal structures demonstrates the efficacy of our LLM-driven approach in improving data-based causal discovery, along with its robustness to inaccurate LLM-derived priors. Codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tyMadara/LLM-CD</uri>.

Causal association between long-term exposure to air pollution and incident Parkinson’s disease

Confusion still exists about the relationships between sociodemographics and adult physical activity behavior. In this paper, data from a national sample of 19,110 adults is examined to determine which of the sociodemographic variables are associated with physical activity behavior, their relative importance, and why these associations may exist. Once one controls for multicollinearity among the sociodemographic variables only age, education, and income maintain any substantive correlation. However, the amount of variance explained is small (R 2 adj = 8%). Two causal models are presented to account for the education: physical activity variance. The relationship is believed to be a spurious one in that there is some other third variable(s) which is speculated to affect both schooling and physical activity behavior. Exposure to sports and physical activity in the schools, though, may enhance the skills necessary for many activities providing an argument for a direct effect of education. Understandi...