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#causality

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Continued thread
jan
”Prehension unifies causality, perception, and memory into a single notion of “feeling.” …our bodies feel causal efficacy directly. When you flick a light switch and your pupils contract, that is causality-in-experience. …feeling, aim, and purpose arise within nature, not by external imposition.”
—Matthew Segall, Prehensions, Propositions, and the Cosmological Commons
#whitehead #prehension #causality #perception #memory
#memory
jan
The assumption is that there are certain physical laws (‘equations’) that all matter must obey, epitomized in the Newtonian paradigm. In sum, this is an ‘upward’ theory of causation.
—Aloisius H. Louie, More Than Life Itself: A Synthetic Continuation in Relational Biology
#assumption #causality
#causality
jan
The difference between the perspectives of classical physics and those of the Copenhagen School was therefore rooted in different views of the basic philosophical concepts: objectivity, phenomenon, causality and physical reality. The actual revolution in the philosophical foundations of physics consisted in Bohr’s seeing himself as obliged to redefine these concepts in order to retain them within the framework of the new physics. At the same time this meant a redefinition of the criteria of science.
—Suzanne Gieser, The Innermost Kernel
#bohr #physics #objectivity #phenomenon #causality #reality
#reality
Dennis Alexis Valin Dittrich

Beyond #causality
aeon.co/essays/to-better-under

#Mathematics shows that understanding isn't just about finding causes, but about recognizing complex structural relationships that connect mind and nature. It shows that the mental and physical worlds aren't separate - they're different aspects of the same reality.

So, stop seeing #science and #humanities as competing worldviews. They're complementary ways of exploring our interconnected world, with mathematics as the translator between them.

AeonTo better understand the world, follow the paths of mathematics | Aeon EssaysIn order to bridge the yawning gulf between the humanities and the sciences we must turn to an unexpected field: mathematics
#Philosophy#Interdisciplinary
DSLC Videos

Recent @DSLC club meetings:

:rstats: The Effect: Instrumental Variables youtu.be/YNR4hNTRSeE #RStats #causal #causality

From the @DSLC :rstats:​chives:

:rstats: "R for Data Science: Communicate Part 2" youtu.be/qQrnNef9fkM #RStats

:rstats: "Health Metrics and the Spread of Infectious Diseases: Intro to Health Metrics (health_metrics1 2)" youtu.be/25Npx53f7F4 #RStats

Visit dslc.video for hours of new #DataScience videos every week!

YouTubeThe Effect: An Introduction to Research Design and Causality: Instrumental Variables (effect01 19)By Data Science Learning Community Videos
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Alexander

Our new article is now on arXiv:
**The Case for Time in Causal DAGs**
doi.org/10.48550/arXiv.2501.19

We propose an explicit notion of time for the variables in causal DAGs and argue that this is essential for interpreting causal relationships and for assessing the applicability of DAGs as causal models.
Our work breaks with the "nontemporal" interpretation of causal DAGs and positions them closer to the potential outcomes framework and time-series causality.
Personally, I feel like the ideas that have culminated in this work have finally put me at ease with causal DAGs, which had always seemed somewhat metaphysical to me before.

Feel free to reach out if you would like to discuss!
#causality #statistics #DAG

arXiv.orgThe Case for Time in Causal DAGsWe make the case for incorporating time explicitly into the definition of variables in causal directed acyclic graphs (DAGs). Causality requires that causes precede effects in time, meaning that the causal relationships between variables in one time order may not be the same in another. Therefore, any causal model requires temporal qualification. We formalize a notion of time for causal variables and argue that this resolves existing ambiguity in causal DAGs and is essential to assessing the validity of the acyclicity assumption. If variables are separated in time, their causal relationship is necessarily acyclic. Otherwise, acyclicity depends on the absence of any causal cycles permitted by the time order. We introduce a formal distinction between these two conditions and lay out their respective implications. We outline connections of our contribution with different strands of the broader causality literature and discuss the ramifications of considering time for the interpretation and applicability of DAGs as causal models.
DSLC Videos

Recent @DSLC club meetings:

:rstats: The Effect: Difference-in-Differences youtu.be/lvIeF3koPAU #RStats #causal #causality

:python: Practical Deep Learning for Coders: Super-resolution youtu.be/pde30NExC4I #PyData #DeepLearning #AI

From the @DSLC :rstats:​chives:

:rstats: "Web APIs with R: How can I get started with APIs? Part 2" youtu.be/WGxr4BTP75w #API #APIs #RStats

Visit dslc.video for hours of new #DataScience videos every week!

YouTubeThe Effect: An Introduction to Research Design & Causality: Difference-in-Differences (effect01 18)By Data Science Learning Community Videos
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