Karthika Mohan

Asst. Professor (Computer Science), Oregon State University
Postdoc UC Berkeley, PhD UCLA (J Pearl)
Artificial Intelligence, Causal Inference, Graphical Models

[Karthika Mohan @Carthica]

@_MargaritaMB 🎉 Hearty Congratulations! 🥳 Wishing you continued success! 👏😊

[Karthika Mohan @Carthica]

🚀 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

[Karthika Mohan @Carthica]

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

[Karthika Mohan @Carthica]

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

[Karthika Mohan @Carthica]

@devendratweetin @EricREaton Thank you for the shoutout! Excited to give my talk this February at Continual Causality, AAAI-25. Looking forward to it!

[Siyuan Guo @syguoML]

New preprint: Do Finetti w/@zcccucla, @Carthica, @fhuszar, @bschoelkopf and me.
https://t.co/SAUs8pwEgM
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]
🧵👇

[Karthika Mohan @Carthica]

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:
https://t.co/8KYcuTPtXX

[Karthika Mohan @Carthica]

@yudapearl This is truly saddening. I recall how much you enjoyed his book and recommended it to me.

[Karthika Mohan @Carthica]

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

[Karthika Mohan @Carthica]

@JKugelgen @jmhernandez233 @RavikumarPrad Congratulations Julius!

[Karthika Mohan @Carthica]

@PhillipHeiler @DaliaAGhanem I look forward to reading this paper!

[Karthika Mohan @Carthica]

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.

https://t.co/lU5A9AhY85

[Judea Pearl @yudapearl]

Remember PO's slogan "causal inference is a missing-data problem"? Well, here @Carthica shows the opposite: "missing-data is a causal problem": https://t.co/6qemKlVZQH. Her gentle introduction through simple and meaningful examples should convince everyone, including the staunchest statistician.

[Karthika Mohan @Carthica]

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 @Carthica]

@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.

[Karthika Mohan @Carthica]

@HaymanJapan1993 Thanks much! Glad that you enjoyed the talk.

[Karthika Mohan @Carthica]

@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, https://t.co/XSYy3D4maJ

[Karthika Mohan @Carthica]

@yudapearl do you understand the analogy to a right angled triangle that Don Rubin makes in his interview:
https://t.co/vjf6AGXi4e,
on pages 88 and 89 ??? I am bewildered.

[Karthika Mohan @Carthica]

@JorisMooij Wonderful read! Thank you for sharing.

[Karthika Mohan @Carthica]

@brianchristian @theNASEM @SchmidtFutures Congratulations Brian!