Forest tree breeding using genomic Markov causal models: a new approach to genomic tree breeding improvement
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Causation versus prediction in travel mode choice modeling
This study discusses and analyzes the difference between causal and predictive modeling to model travel mode choice. Causal modeling is expressed through causal discovery and causal inference, used to extract causal relationships in mode choice decision making and estimate causal effects between variables. Predictive modeling is expressed through artificial neural networks. When modeling travel mode choice in three Chicago neighborhoods, we find that both causal and predictive modeling approaches perform well and are useful for their modeling purposes. We also note that the study of mode choice behavior through causal modeling is under-explored while it could transform our understanding of mode choice behavior. Further research is needed to realize the full potential of these techniques in modeling mode choice.
Dynamical reversibility and a new theory of causal emergence based on SVD
The theory of causal emergence (CE) with effective information (EI) posits that complex systems can exhibit CE, where macro-dynamics show stronger causal effects than micro-dynamics. A key challenge of this theory is its dependence on coarse-graining method. In this paper, we introduce a fresh concept of approximate dynamical reversibility derived from the singular value decomposition(SVD) of the Markov chain and establish a novel framework for CE based on this. We find that the essence of CE lies in the presence of redundancy, represented by irreversible and correlated information pathways within the Markov dynamics. Therefore, CE can be quantified as the potential maximal efficiency increase for dynamical reversibility or information transmission. We also demonstrate a strong correlation between the approximate dynamical reversibility and EI, establishing an equivalence between the SVD and EI maximization frameworks for quantifying CE, supported by theoretical insights and numerical examples from Boolean networks, cellular automata, and complex networks. Importantly, our SVD-based CE framework is independent of specific coarse-graining techniques and effectively captures the fundamental characteristics of the dynamics.
Triangulation for causal loop diagrams: constructing biopsychosocial models using group model building, literature review, and causal discovery
The complex nature of many health problems necessitates the use of systems thinking tools like causal loop diagrams (CLDs) to visualize the underlying causal network and facilitate computational simulations of potential interventions. However, the construction of CLDs is limited by the constraints and biases of specific sources of evidence. To address this, we propose a triangulation approach that integrates expert and theory-driven group model building, literature review, and data-driven causal discovery. We demonstrate the utility of this triangulation approach using a case example focused on the trajectory of depressive symptoms in response to a stressor in healthy adults. After triangulation with causal discovery, the CLD exhibited (1) greater comprehensiveness, encompassing multiple research fields; (2) a modified feedback structure; and (3) increased transparency regarding the uncertainty of evidence in the model structure. These findings suggest that triangulation can produce higher-quality CLDs, potentially advancing our understanding of complex diseases.
Current inequality and future potential of US urban tree cover for reducing heat-related health impacts
Excessive heat is a major and growing risk for urban residents. Here, we estimate the inequality in summertime heat-related mortality, morbidity, and electricity consumption across 5723 US municipalities and other places, housing 180 million people during the 2020 census. On average, trees in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05 °C more than in POC neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity consumption than in POC neighborhoods. We estimate that an ambitious reforestation program would require 1.2 billion trees and reduce population-weighted average summer temperatures by an additional 0.38 ± 0.01 °C. This temperature reduction would reduce annual heat-related mortality by an additional 464 ± 89 people, annual heat-related morbidity by 80,785 ± 6110 cases, and annual electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by 23.7 ± 1.2 MtCO2e yr−1 and decreasing annual electricity-related GHG emissions by 2.1 ± 0.2 MtCO2e yr−1. The total economic value of these benefits, including the value of carbon sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would exceed these benefits. The exception is areas that currently have less tree cover, often the majority POC, which tend to have a relatively high return on investment from tree planting.
Causal chambers as a real-world physical testbed for AI methodology
In some fields of artificial intelligence, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited information about the applicability of the proposed methods to real problems. As a step forward, we have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems. The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a variety of fields. We illustrate potential applications through a series of case studies in fields such as causal discovery, out-of-distribution generalization, change point detection, independent component analysis and symbolic regression. For applications to causal inference, the chambers allow us to carefully perform interventions. We also provide and empirically validate a causal model of each chamber, which can be used as ground truth for different tasks. The hardware and software are made open source, and the datasets are publicly available at causalchamber.org or through the Python package causalchamber.
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