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The collective discussions reveal a strong interest in understanding the capabilities and limitations of causal discovery methods, exemplified by comments supporting that causal models can be learned from observations and references to foundational works like Pearl's *Causality*. The tone varies from cautious skepticism to active curiosity, highlighting that the publication stimulates both validation and further inquiry among participants.
Most comments acknowledge the value of the review, with some highlighting that causal discovery from observational data is possible under certain conditions, showing overall consensus.
Discussions show high curiosity about the methods and theoretical aspects, with mentions of specific models, textbooks, and surveys indicating strong engagement with the topic.
Participants reference specific works, such as Pearl's *Causality* and various surveys, demonstrating active reading, looking up additional resources, and critical analysis.
While some posts emphasize the importance of causal discovery in fields like biology and evidence of ongoing debates, overall the discussions suggest the review influences scholarly understanding rather than immediate practical change.
And another one here: https://t.co/zc3ADVFgn1
@adad8m @sebkrier I won't go into detail, but two examples are implying that DL models cannot learn causal models (they do!) and that more generally one cannot learn causal structure from pure observation (one can under certain conditions! see the review linked). https://t.co/aN9lFaKmuV
@keithschnak If you want theory, you should read Pearl's Causality, Chapter 2 (I guess). If you want more applied stuff, https://t.co/DAEKhfrSYE
Causality in gene networks. We MUST do it https://t.co/slGWNT3Ouy
"Practical (causal) analysis is not a matter of pressing a few buttons" On the difficulties of automating/routinising analysis, from this nice review of causal discovery methods. https://t.co/Sraud4V9x8 https://t.co/JVEWaSlgd1
Even prominent CI people https://t.co/UXFXvaFN5l note that CI is misleading unless very strong assumptions are met, and again these assumptions are hard to understand, hard to verify and seldom actually satisfied. 3/
This video explores methods for discovering causal relationships from observational data, including constraint-based, score-based, and functional causal models, with practical examples in Python and resources for…
Deep Learning is promising, but we're still far from AGI. This video explores research directions like higher-level cognition, generalization, causality, and brain-inspired learning approaches that could bring us closer…
RT @vitalinew2: https://t.co/5LJZplBdsb https://t.co/8ZURcsjI2G
https://t.co/5LJZplBdsb https://t.co/8ZURcsjI2G
@adad8m @sebkrier I won't go into detail, but two examples are implying that DL models cannot learn causal models (they do!) and that more generally one cannot learn causal structure from pure observation (one can under certain conditions! see the review linked). https://t.co/aN9lFaKmuV
Even prominent CI people https://t.co/UXFXvaFN5l note that CI is misleading unless very strong assumptions are met, and again these assumptions are hard to understand, hard to verify and seldom actually satisfied. 3/
@keithschnak If you want theory, you should read Pearl's Causality, Chapter 2 (I guess). If you want more applied stuff, https://t.co/DAEKhfrSYE
@sp_monte_carlo For overviews of causal discovery methods from PC and beyond, see https://t.co/oErNtQZY8b or the very accessible Simons Bootcamp lectures by @d_malinsky https://t.co/cWdF1v6pwf
@sp_monte_carlo Oh! I vaguely remember a paper that was evaluating exactly that but I don't have the link anymore (will try to search later). I do remember reading something about it in a survey [1] that points to this [2] [1] https://t.co/tIfBYufiCu [2] https://t.co/go6DGIkWih
Causality in gene networks. We MUST do it https://t.co/slGWNT3Ouy
And another one here: https://t.co/zc3ADVFgn1
"Practical (causal) analysis is not a matter of pressing a few buttons" On the difficulties of automating/routinising analysis, from this nice review of causal discovery methods. https://t.co/Sraud4V9x8 https://t.co/JVEWaSlgd1
@gungnir64405851 Jag kom på två områden där filosoferna har kunnat ha en mer aktiv roll och faktiskt kunnat föreslå lösningar till empiriska forskare. 1) Causal discovery https://t.co/c4ZQldOoQf En av författaren är C. GLymour: en av mina största inspirationskällor.
@athie_gnome @rasmansa @gibletsMoon https://t.co/9fD5Ph7Tin
https://t.co/8ETHqdfA9H
@vallens @alexpghayes The original paper is a bit obscure, Spirtes 2000. https://t.co/aHvgbz20AI seems to be a reasonable review. Not sure of a good textbook treatment of it...
Review of Causal Discovery Methods Based on Graphical Models https://t.co/B9Jy2kh5te
妥協して、PCアルゴリズムはこれを見るだけでもいいかなぁ... https://t.co/mN0N2SApLI
@yudapearl So something like this? https://t.co/UtudvEm3UL
RT @FrontGenetics: New Research: Review of Causal Discovery Methods Based on Graphical Models: A fundamental task in various disciplines of…
doi: 10.3389/fgene.2019.00524. N. Gnecco, N. Meinshausen, J. Peters, and S. Engelke. Causal discovery in heavy-tailed. models. The Annals of Statistics, 49 ...
1–15, [Link]/articles/10.3389/fgene.2019.00524/full. Gomes, J., L. Kogan, and L. Zhang (2003): “Equilibrium Cross Section of. Returns.” Journal of Political ...
DOI: 10.3389/fgene.2019.00856. High-quality draft genome sequence of ... DOI: 10.3389/fpsyt.2019.00524. Faulty neuronal determination and cell ...
https://doi.org/10.3389/fgene. 2019.00524 12. Hamill, L.: Agent-based modelling: The next 15 years. J. Artif. Societies Soc. Simul. 13(4), 7 (2010). https ...
www.frontiersin.org/articles/10.3389/fgene.2019.00524/full. Gospodinov, N., R. Kan, and C. Robotti (2014): Misspecification-Robust Inference in Linear. Asset ...
https://www.frontiersin.org/articles/10.3389/fgene.2019.00524/full. Harvey, C., Y. Liu, and H. Zhu (2016): “… and the cross-section of expected returns ...
www.frontiersin.org/article/10.3389/fgene.2019.00524/full. 24. Silva R. Observational-interventional priors for dose-response learning. in 43. Tennant PW ...
Graphical Models.” Frontiers in Genetics, Vol. 10, No. 524, pp. 1–15, www.frontiersin.org/articles/10.3389/fgene.2019.00524/full.
doi: 10.3389/fgene.2019.00524 50. Sureda A. Bibiloni MdM, Julibert A, Bouzas C, Argelich E, Llompart I, et al. Adherence to the mediterranean diet and ...
10.3389/fgene.2019.00524. 69. Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences ...
doi: 10.3389/fgene.2019.00524. Hajat, C. , & Stein, E. (2018). The global burden of multiple chronic conditions: A narrative review. Preventive Medicine ...
doi:10.3389/fgene.2019.00524. Granger, C. W. J. 1969. “Investigating Causal Relations by Econometric Models and Cross-. Spectral Methods.” Econometrica 37 (3): ...
doi: 10.3389/fgene.2019. 00524. URL https://www.frontiersin.org/articles/10.3389/fgene.2019.00524. [5] P. Spirtes, C. Glymour, and R ...
... 10.3389/fgene.2019.00524/full. A couple books I'd recommend: for general causal inference principles, start with Pearl's Causal Inference in ...
... 10.3389/fgene.2019.00524/full. Um par de livros que eu recomendaria: para princípios gerais de inferência causal, comece com Causal Inference ...
... 10.3389/fgene.2019.00524/full. Quelques livres que je recommanderais : pour les principes généraux de l'inférence causale, commencez par ...
DOI: 10.3389/fgene.2019.00856. High-quality draft genome sequence of ... DOI: 10.3389/fpsyt.2019.00524. Faulty neuronal determination and ...
https://doi.org/10.3389/fgene.2019.00524. [3] ML based causal discovery: arXiv:1803.04929 [stat.ML] (good primer). [4] Causal Discovery ...
https://doi.org/10.3389/fgene.2019.00524. [3] ML based causal discovery: arXiv:1803.04929 [stat.ML] (good primer). [4] Causal Discovery ...
'Review of Causal Discovery Methods Based on Graphical Models'. Frontiers in Genetics 10. https://doi.org/10.3389/fgene.2019.00524. Mani, ...
... 10.3389/fgene.2019.00524/full , so here I would say again: specialised ML methods may actually help to infer causality better than OLS. I ...
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