Assimilative Causal Inference: Identifying Causes Backwards from Observed Effects скачать в хорошем качестве

Assimilative Causal Inference: Identifying Causes Backwards from Observed Effects

(Greek and English subtitles are available; click on the CC button to activate.) ―――― Abstract ―――― Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems. Assimilative causal inference (ACI) is a new framework that leverages data assimilation to trace causes backwards from observed effects. ACI reformulates causality as an inverse problem of uncertainty quantification rather than assessing forward influence. It uniquely identifies dynamic causal structures in real time without requiring observations of candidate causes, while scaling efficiently to high dimensions. Crucially, it facilitates mathematically rigorous measures for the forward and backward causal influence range (CIR) of a relationship; the forward CIR quantifies the temporal impact of a cause, while the backward CIR traces the onset of triggers for an observed effect, thus characterizing causal predictability and attribution of outcomes over time, respectively. ―――― Video Chapters ―――― 00:00 - Introduction 00:45 - Traditional Causal Inference Methods 01:53 - The Assimilative Causal Inference (ACI) Framework 03:46 - A Brief Overview of Data Assimilation 04:39 - ACI as a Bayesian Inverse Problem 05:22 - Conditional ACI 05:41 - Causal Influence Ranges in ACI 06:17 - Conclusion _________________________________________________________ ► Acknowledgements: My academic advisor is Prof. Nan Chen - https://people.math.wisc.edu/~nchen29. I'm supported as a research assistant under his grants. Animations were made using Manim Community Edition and DaVinci Resolve. ► More Information: https://mariosandreou.short.gy/ACI ► Assimilative Causal Inference Preprint (Co-authored with Prof. Nan Chen and Prof. Erik Bollt): https://doi.org/10.48550/arXiv.2505.1... ► Causal Influence Range Preprint (Co-authored with Prof. Nan Chen): https://doi.org/10.48550/arXiv.2510.2... ► References and Sources: ● Background Music – "Vibing Over Venus", Kevin MacLeod (https://incompetech.com) | Licensed under Creative Commons: By Attribution 3.0 (http://creativecommons.org/licenses/b...) ● Detailed View of Arctic Sea Ice – NASA image (NASA Identifier: ge_07370) by Glenn Research Center, based on Landsat-7 data from the Global Land Cover Facility (https://commons.wikimedia.org/wiki/Fi...) ● Satellite 3D Model – By printable_models on free3d.com (https://free3d.com/3d-model/satellite...) ● Natural Earth Texture with Edited Clouds – Tom Patterson (https://www.shadedrelief.com) ● Hurricane Katrina Image - NASA image of Hurricane Katrina on August 28, 2005 (https://en.wikipedia.org/wiki/Hurrica...) ► Personal Website: https://mariosandreou.short.gy/Homepage Corrections: 02:20 Strictly speaking, for our setting, the diffusion feedback matrix Σˣ in the observable process needs to be independent of the unobserved state y. This ensures a well-posed data assimilation problem where the conditional distribution of y given the data of x (posterior) contains all available information about y. This is a classical necessary assumption in nonlinear filtering (e.g., see "The Oxford Handbook of Nonlinear Filtering").