@_MargaritaMB 🎉 Hearty Congratulations! 🥳 Wishing you continued success! 👏😊
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Karthika Mohan
83 posts
Karthika Mohan
@Carthica
Asst. Professor (Computer Science), Oregon State University Postdoc UC Berkeley, PhD UCLA (J Pearl) Artificial Intelligence, Causal Inference, Graphical Models
Corvallis, Oregon Katılım Ekim 2010
523 Takip Edilen1.2K Takipçiler
Thrilled to have received this funding for my causal inference research program - with my amazing team we shall continue to advance methods and provide expertise in this key area for another 5 years!!! 🥳☺️
ViCBiostat@vicbiostat
Congratulations to Margarita Moreno-Betancur and Julie Simpson who have both received 2025 NHMRC Investigator Grants 👏 @_MargaritaMB @JulieASimpson50 @CEBU_Melbourne @MischHub vicbiostat.org.au/news
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🚀 Excited to be at AAAI! 🚀 Join me on Tuesday, 25th Feb to explore the fascinating ways causality brings clarity to messy datasets. See you in Philadelphia! 👋#AAAI2025 #CausalDataScience #MissingData #noniidData
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Honored to speak on causality at #IBC2024 in Atlanta! 🎉 Humbled to hear my work on missing data has helped improve outcomes for children. 🙏✨ Huge shout-out to the researchers at Murdoch Children's Research Institute! 👏🌟 @yudapearl @_MargaritaMB @JiaxinZhang96 @ghazalehdashti
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Challenging the IID assumption is key to advancing AI/ML, as real-world data often violates IID. We are excited to present our paper that explores this frontier.
Siyuan Guo@syguoML
Do Finetti: On Causal Effects for Exchangeable Data. #NeurIPS2024 Oral Session 5B: Fri 13 Dec 10 a.m. — 11 a.m. PST Poster: West Ballroom A-D #5000 Fri 13 Dec 11 a.m. — 2 p.m. PST joint work with @zcccucla @Carthica @fhuszar @bschoelkopf
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@devendratweetin @EricREaton Thank you for the shoutout! Excited to give my talk this February at Continual Causality, AAAI-25. Looking forward to it!
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Looking forward to all your amazing submissions. Also featuring invited talks by @Carthica #ShengLi and @EricREaton plus a challenge on continual confounding. Exciting time ahead !!
Martin Mundt@mundt_martin
Our Continual Causality Bridge is going into its 3rd edition at AAAI-25 @RealAAAI ! 🎉 Join us for a spectacular 2 days of continual learning & causality by submitting your original 4 page work (for proceedings or non-archival tracks) until Nov 25 AOE! continualcausality.org/cfp/
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Karthika Mohan retweetledi
New preprint: Do Finetti w/@zcccucla, @Carthica, @fhuszar, @bschoelkopf and me.
arxiv.org/abs/2405.18836
Do Finetti provides a do-calculus foundation for exchangeable data following the independent causal mechanism (ICM) principle + a causal Pólya urn model to show how interventions propagate effects in exchangeable settings. [1/n]
🧵👇
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We invite submissions of extended abstracts, up to 2 pages in length, to the UAI Causal Inference Workshop.
Sara Magliacane hiring PhDs at UvA@saramagliacane
Excited to announce the 9th #causality workshop at @UncertaintyInAI with @raziehnabi @Carthica @CalebMiles16 @d_malinsky 🎉 Do you have interesting work in #causality #causalInference #causalDiscovery #CRL etc? Consider submitting an abstract by May 31st: sites.google.com/view/causaluai…
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@yudapearl This is truly saddening. I recall how much you enjoyed his book and recommended it to me.
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I am shocked, can't find the right words -- will be back when/if I find them.
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Israeli American economist and Nobel laureate Daniel Kahneman dies at 90 haaretz.com/israel-news/20…
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I discussed the application of Operations Research methods to the challenge of Causal Discovery with Imperfect Data at the Computing Community Consortium (CCC) AI/OR Workshop in Washington DC. Grateful for the opportunity and huge thanks to the fantastic organizers!
Thiago Serra (@thserra.bsky.social)@thserra
In the third technical presentation of the AI / OR workshop, Karthika Mohan talks about how to use integer programming for causal inference over incomplete data #orms #monarchsofmip
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On Friday, I successfully defended my PhD thesis "Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment" in Cambridge, examined by Profs @jmhernandez233 and @RavikumarPrad. I'm particularly delighted to have passed with no corrections 🎉
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@PhillipHeiler @DaliaAGhanem I look forward to reading this paper!
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A very interesting and important contribution by @DaliaAGhanem and co-authors. There is no agreement on tests for internal validity in both experimental and observational studies with attrition. 1/8
Dalia Ghanem@DaliaAGhanem
Attrition is common in many field experiments and can threaten their internal validity. 1/n
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Enjoying every page of this book! Delighted by the dedicated chapter on causality, thrilled with the missing data section, and grateful for the shoutout to my work with @yudapearl . A must-read for AI students.
linkedin.com/posts/alan-mac…
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Karthika Mohan retweetledi
Remember PO's slogan "causal inference is a missing-data problem"? Well, here @Carthica shows the opposite: "missing-data is a causal problem": ucla.in/3sxeqOW. Her gentle introduction through simple and meaningful examples should convince everyone, including the staunchest statistician.
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Congratulations to Dr. Chi Zhang (@zcccucla) on defending her PhD thesis! Her work on interference pushes research boundaries and highlights the perils of blindly assuming IID. Well done, Dr. Zhang! It's been a delightful journey collaborating with you. 🥳🎉🍾
@yudapearl @oacarah
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@AngeloDalli @yudapearl O and Ro are parents of O*. You can model the scenario by adding two edges, one between U & O and the other between U & Ro.
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Was exploring missingness graphs - if you have an *exogenous* var U (possibly U can be guesstimated) suspected to have a causal link to the obs proxy O* of some var O with missing data in the model, how would you model U? What can you learn about U reliably? @Carthica @yudapearl
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@raziehnabi @analisereal @noah_greifer @stephenjwild @r0ntu Agree! However, recoverability is query dependent. In X-->Y<-->Rx, P(X) is recoverable (despite Y being a collider between X and Rx) but P(Y,X) is not. Reference Mohan & Pearl, Neurips-14, proceedings.neurips.cc/paper/2014/has…
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@analisereal @noah_greifer @stephenjwild @r0ntu @Carthica I agree with what @analisereal said above. In general, having a collider path between a variable and its corresponding missingness indicator is no good news.
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I'm in a really weird case where I'm interested in the effect of a missingness indicator on an outcome within strata of the variable with missingness (S). The outcome is highly correlated with S, so I wanted to use it to impute S. Is this a problem?
#causaltwitter
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@yudapearl do you understand the analogy to a right angled triangle that Don Rubin makes in his interview:
muse.jhu.edu/article/867089…,
on pages 88 and 89 ??? I am bewildered.
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Highly recommended reading: interviews with 4 causality pioneers Heckman, Robins, Pearl & Rubin in Journal of Observational Studies. muse.jhu.edu/issue/48885
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Excited to announce that I’m one of the winners of the Awards for Excellence in Science Communication, by @theNASEM and @SchmidtFutures. #SciCommFutures Honored to be in such great company, and encouraged to see AI ethics and safety among the vital questions of our time.
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