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1. Irreversible (One-hit) and Reversible (Sustaining) Causation

   Ross, Lauren N. (July 2022, Philosophy of science)

   Potochnik, Angela (Ed.)

   This paper explores a distinction among causal relationships that has yet to receive attention in the philosophical literature, namely, whether causal relationships are reversible or irreversible. We provide an analysis of this distinction and show how it has important implications for causal inference and modeling. This work also clarifies how various familiar puzzles involving preemption and over-determination play out differently depending on whether the causation involved is reversible. 

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2. Training Regimes and Skill Formation in France and Germany An Analysis of Change Between 1970 and 2010

   https://doi.org/10.1093/sf/soaa037  

   Elbers, Benjamin; Bol, Thijs; DiPrete, Thomas A (May 2020, Social Forces)

   Abstract How do educational systems prepare workers for the labor market? Stratification research has often made a distinction between two ideal-types: “qualificational spaces,” exemplified by Germany with a focus on vocational education, and “organizational spaces,” exemplified by France with a focus on general education. However, most studies that investigated this distinction did so by focusing only on the size of the vocational sector, not on whether graduates with a vocational degree actually link strongly to the labor market. Moreover, they often studied male workers only, ignoring potential gender differences in how school-to-work linkages are established. In this paper, we map the change in education–occupation linkage in France and Germany between 1970 and 2010 using an approach that can distinguish between changes in rates and changes in the structure of school-to-work linkages. Surprisingly, we find that the German vocational system in 1970 was not, on average, substantially more efficient in allocating graduates to specific occupations than the French system. This finding is a major departure from earlier results, and it shows that the differences between 1970’s France and Germany, on which the qualificational-organizational distinction is based, are smaller than previously assumed. Partly, this is due to the fact that the female labor force was omitted from earlier analyses. We thus show that ignoring the female workforce has consequences for today’s conception of skill formation systems, particularly because a large share of educational expansion is caused by an increase in female enrollment in (higher) education. 

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3. Causal models with constraints

   S. Beckers; C. Hitchcock; J. Y. Halpern (April 2023, Proceedings of the Second Conference on Causal Learning and Reasoning)

   Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables LDL, HDL, and TOT that represent the level of low-density lipoprotein cholesterol, the level of lipoprotein high-density lipoprotein cholesterol, and total cholesterol level, with the relation LDL+HDL=\OT. This cannot be done in standard causal models, because we can intervene simultaneously on all three variables. The goal of this paper is to extend standard causal models to allow for constraints on settings of variables. Although the extension is relatively straightforward, to make it useful we have to define a new intervention operation that disconnects a variable from a causal equation. We give examples showing the usefulness of this extension, and provide a sound and complete axiomatization for causal models with constraints. 

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4. Advancing Medical Video Question Answering Through Large Language Models, Temporal Localization and Causal Reasoning

   Demirhan, Hilmi; Gopalakrishnan, Seetha; Zadrozny, Wlodek; Dou, Wenwen (May 2025, 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications)

   In this research, we take an innovative approach to the Video Corpus Visual Answer Localization (VCVAL) task using the MedVidQA dataset. We expand on it by incorporating causal inference for medical videos, a novel approach in this field. By leveraging the state-of-the-art GPT-4 and Gemini Pro 1.5 models, the system aims to localize temporal segments in videos and analyze cause-effect relationships from subtitles to enhance medical decision-making. This paper extends the work from the MedVidQA challenge by introducing causality extraction to enhance the interpretability of localized video content. Subtitles are segmented to identify causal units such as cause, effect, condition, action, and signal. Prompts guide GPT-4 and Gemini Pro 1.5 in detecting and quantifying causal structures while analyzing explicit and implicit relationships, including those spanning multiple subtitle fragments. Our results reveal that both GPT-4 and Gemini Pro 1.5 perform better when handling queries individually but face challenges in batch processing for both temporal localization and causality extraction. Despite these challenges, our innovative approach has the potential to significantly advance the field of Health Informatics. In this research, we address the Video Corpus Visual Answer Localization (VCVAL) task using the MedVidQA dataset and take it a step further by integrating causal inference for medical videos. By leveraging the state-of-the-art GPT-4 and Gemini Pro 1.5 model, our system is designed to localize temporal segments in videos and analyze cause-effect relationships from subtitles to enhance medical decision-making. Our preliminary results indicate that while both models perform well for some videos, they face challenges for most, resulting in varying performance levels. The successful integration of temporal localization with causal inference can provide significant improvement for the scalability and overall performance of medical video analysis. Our work demonstrates how AI systems can uncover valuable insights from medical videos, driving significant progress in medical AI applications and potentially making significant contributions to the field. 

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5. Investigating environmental effects on phonology using diachronic models

   https://doi.org/10.1017/ehs.2023.33  

   Hartmann, Frederik; Roberts, Seán G.; Valdes, Paul; Grollemund, Rebecca (January 2024, Evolutionary Human Sciences)

   Mace, Ruth (Ed.)

   Abstract Previous work has proposed various mechanisms by which the environment may affect the emergence of linguistic features. For example, dry air may cause careful control of pitch to be more effortful, and so affect the emergence of linguistic distinctions that rely on pitch such as lexical tone or vowel inventories. Criticisms of these proposals point out that there are both historical and geographic confounds that need to be controlled for. We take a causal inference approach to this problem to design the most detailed test of the theory to date. We analyse languages from the Bantu language family, using a prior geographic–phylogenetic tree of relationships to establish where and when languages were spoken. This is combined with estimates of humidity for those times and places, taken from historical climate models. We then estimate the strength of causal relationships in a causal path model, controlling for various influences of inheritance and borrowing. We find no evidence to support the previous claims that humidity affects the emergence of lexical tone. This study shows how using causal inference approaches lets us test complex causal claims about the cultural evolution of language. 

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