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#causality

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”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

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

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.

Recent @DSLC club meetings:

:python: Practical Deep Learning for Coders: Chapter 16 "The Learner framework" youtu.be/e9VFOpn2hmA #PyData #DeepLearning #AI

:rstats: The Effect: 15 "Simulation" youtu.be/amymCljDt3k #RStats #causal #causality

:rstats: ggplot2: Elegant Graphics for Data Analysis: 15 "Coordinate systems" youtu.be/Ymj-4YggV3Q #RStats #DataViz #ggplot2

Subscribe at DSLC.video for hours of #DataScience videos every week!

Recent @DSLC club meetings:

:python: Practical Deep Learning for Coders: 10 "Diving Deeper" youtu.be/6_aM714giok #PyData #DeepLearning #AI

:rstats: ggplot2: Elegant Graphics for Data Analysis: 10 "Position scales and axes" youtu.be/vaXDjSMhcx8 #RStats #DataViz #ggplot2

:rstats: The Effect: 10 "Treatment Effects" & Chapter 11 "Causality with Less Modeling" youtu.be/G3Ur3lZAYCs #RStats #causal #causality

Subscribe at DSLC.video for hours of #DataScience videos every week!

Recent @DSLC club meetings:

:python: Practical Deep Learning for Coders: "Data ethics" youtu.be/rTkF5INDV5I #PyData #DeepLearning #AI

:rstats: ggplot2: 7 "Networks" youtu.be/1uwJaDIzdCg #RStats #DataViz #ggplot2

:rstats: The Effect: Intro to Research Design and Causality: 8 "Causal Paths and Closing Back Doors" youtu.be/OUP-3Ut6Xno #RStats #causal #causality

Subscribe at DSLC.video for hours of #DataScience videos every week!

Participate at DSLC.io

Recent @DSLC club meetings:

:rstats: Advanced R: Chapter 12 "Base types" youtu.be/6yW9dlErLv0 #RStats

:rstats: The Effect: An Introduction to Research Design and Causality: Chapter 6 "Causal Diagrams" youtu.be/YIxO_L728mg #RStats #causal #causality

:rstats: :python: Mastering Shiny: Chapter 11 "Bookmarking" youtu.be/g7M1n3jaA9c

Subscribe at DSLC.video for hours of #DataScience videos every week!

Participate in @DSLC book clubs at DSLC.io

Recent @DSLC club meetings:

:python: Practical Deep Learning for Coders: 6 "Random forests" youtu.be/NpekIIW87Yw #DeepLearning #AI

:rstats: The Effect: 3 "Describing Variables" & 4 "Describing Relationships" youtu.be/v2xR2RcAjOo #causality

:rstats: ggplot2: 4 "Collective geoms" youtu.be/BCweZnoLJng #DataViz #ggplot2

Subscribe at DSLC.video for hours of #DataScience videos every week!

Participate in @DSLC book clubs at DSLC.io