Asst. Professor (Computer Science), Oregon State University Postdoc UC Berkeley, PhD UCLA (J Pearl) Artificial Intelligence, Causal Inference, Graphical Models
Corvallis, Oregon
Joined October 2010
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People, Causal Learning and Reasoning (CLeaR) 2027's Call for Papers is here! 📣✨cclear.cc/2027 And this time, we’re heading to 𝗣𝗮𝗿𝗶𝘀, to the historic, and charming Sorbonne University. 🇫🇷🏛️ The organizing committee is pretty fab, if I may say so myself 😄 Submit your work, join the conversations, and come be part of CLeaR 2027!
Judea Pearl surrounded by his (academic) children at a celebration of his 90th birthday! 🎉🎂❤️ Many thanks to Rina, Hector, and Elias for organizing the event, and to Euncie for the wonderful photo. @yudapearl
🤔 Ever wondered how to test a causal graph when some variables are corrupted by measurement error? Our paper derives novel testable implications for this setting, inspired by @yudapearl’s work on measurement error. Enjoy! 📄✨ karthikamohan.com/PGM_Camera…
🚀 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
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
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.
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
Karthika Mohan retweeted
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]
🧵👇
We invite submissions of extended abstracts, up to 2 pages in length, to the UAI Causal Inference Workshop.
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/causal…
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!
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
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-mack…
Karthika Mohan retweeted
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.
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
Karthika Mohan retweeted
Hear Hear 📢📢📢 Excited to announce the First EPFL Causality Workshop, sponsored by the EPFL School of Engineering #epflSTI.
First Speaker is Prof. Aapo Hyvärinen, on 'Causal discovery based on latent variable models'.
More Info & (free) Registration👉causality2022.epfl.ch/
Ah! the long-awaited book is here!🥳🥳 For a gentle introduction to missing data from a causal perspective, see my chapter, dl.acm.org/doi/10.1145/35017…
Out at last: 'Probabilistic and Causal Inference: The Works of Judea Pearl' eds. Geffner, Dechter, Halperin, dl.acm.org/doi/book/10.1145/…
I am honored to have contributed Ch. 31, 'The Causal Foundations of Applied Probability and Statistics', dl.acm.org/doi/10.1145/35017…
I welcome PhD applications from students interested in working with me on topics at the intersection of Artificial Intelligence and Causal Inference. Application deadline: 15th Dec 2021. Link: gradschool.oregonstate.edu/p…
I am excited to announce that I will be joining the School of #EECS @OregonState as a tenure-track assistant professor in the Fall. Ever grateful to my mentors, friends & family for their advice, support & encouragement through the years!
Karthika Mohan retweeted
Good news for all victims of missing-data. We are informed that our paper "Graphical Models for Processing Missing Data" (with @Carthica) has pacified all reviewers' objections and will be published in JASA, see ucla.in/2LdEjZW
So, if you are confused, as most people 1/n
Here is a wonderful blog post about missingness graphs that allows one to answer quite a lot of questions about missingness. Great job, @CookieSci! @yudapearl
Oh look, someone wrote a nice blog post about m-graphs! :) jakewestfall.org/blog/index.…
Karthika Mohan retweeted
Deadline extension for our #ICML workshop on missing values to June 8!
Keynotes by M. van der Schaar, R. Caruana @Carthica, @MSadinle, @madeleineudell
Panel discussants will include A. Gelman I. Shpitser
artemiss-workshop.github.io/
w/ @JulieJosseStat @jesfrellsen @GaelVaroquaux
We’re organizing Artemiss @icmlconf : the first workshop on missing data in top ML conferences in recent years!
Artemiss-workshop.github.io
/w @jesfrellsen, @JulieJosseStat and @GaelVaroquaux