Ang Li

Asst. Professor of Computer Science, Florida State University
Graduated Ph.D. @UCLA @yudapearl

[Ang Li @Ang_UCLA]

IJCAI 2026 paper: https://t.co/sBe2af3Wqs
Previous paper:
https://t.co/pOidgJiiXF

[Ang Li @Ang_UCLA]

My PhD @moraxin_ just presented our paper at IJCAI2026! We show that all discrete Probabilities of Causation (PoCs) can be represented using four representative forms and derive closed-form bounds for all four. This work extends my previous work with @yudapearl on nonbinary PoCs.

[Ang Li @Ang_UCLA]

The forecast of a hurricane definitely requires causal inference, as factors such as the hurricane's strength, population density, and city size can act as confounding variables, influencing both its projected landfall location and its potential category at landfall. @yudapearl

[Ang Li @Ang_UCLA]

@yudapearl and I have also had two papers accepted for the AAAI-24 main track, focusing on the non-binary cases of the probabilities of causation and the unit selection problem. Here is the link to the papers:https://t.co/K0A4jrVoWe, https://t.co/4s5gLItQgQ

[Ang Li @Ang_UCLA]

@yudapearl FYI its the workshop W31 at AAAI24 (today), the presentation will be in Room 214 at 3:10PM, here is the link of the paper, https://t.co/YzKa2bHXj6

[Ang Li @Ang_UCLA]

If you are interested in this opportunity, please send me a brief email with your curriculum vitae (C.V.) and any other relevant information. I look forward to hearing from you! @yudapearl

[Ang Li @Ang_UCLA]

I am seeking self-motivated Ph.D. students to join my lab in the Department of Computer Science at Florida State University, starting in the 2024 Spring or Fall. Our research will focus on Artificial Intelligence, Causal Inference, and Causality-based Decision-Making. @yudapearl

[Ang Li @Ang_UCLA]

@soboleffspaces Thank you Boris : )

[Ang Li @Ang_UCLA]

I'm thrilled to announce that I will be joining the Dept. of Computer Science at the Florida State University as a tenure-track assistant professor this fall. I'm deeply grateful to my mentors, friends, and family for their advice, support, and encouragement over the years.

[Ang Li @Ang_UCLA]

@yudapearl Thank you, Judea. I couldn't have completed this journey without your wonderful support and encouragement. I hope that our unit selection model and counterfactual reasoning will elevate personal decision-making to a whole new level.

[Ang Li @Ang_UCLA]

I was fortunate to have had the guidance of Professor Judea Pearl @yudapearl , who led me to the fascinating field of causal inference and taught me how to become a skilled researcher.

[Ang Li @Ang_UCLA]

@yudapearl Happy birthday, Judea, can't wait to work with you for another 86 years : )

[Ang Li @Ang_UCLA]

@RWJE_BA @soboleffspaces @artistexyz @yudapearl @VC31415 @causalinf @smueller In my point,if you want bounds of PNS, or benefit function, then here it is the case. If there is causal structures that are sufficient to determine the exp data, then things are easier, otherwise, we got to find a way to obtain expdata as expdata play major role in those bounds

[Ang Li @Ang_UCLA]

@artistexyz @soboleffspaces @yudapearl @HL327 @MatheusFacure @causalinf @VC31415 @smueller um, here is the link https://t.co/R1YWLB9l4c , but i ll email you one

[Ang Li @Ang_UCLA]

@artistexyz @soboleffspaces @yudapearl @HL327 @MatheusFacure @causalinf @VC31415 @smueller lol, thats true, especially for counterfactual things, sometimes when I looked my old notes, I just couldnt believe i had that....

[Ang Li @Ang_UCLA]

@artistexyz @soboleffspaces @yudapearl @HL327 @MatheusFacure @causalinf @VC31415 @smueller our new aaai22 paper have an example to partial mediator case, prob gonna help? https://t.co/nzvOElUDqI

[Ang Li @Ang_UCLA]

@artistexyz @soboleffspaces @yudapearl @HL327 @MatheusFacure @causalinf @VC31415 @smueller Even it is pure mediator, Z could be affect by exogenous variables u_z. Or the population specific characteristics C may affect Z. So Z still can have multi values, as in theory, I summed over all z and z' where z != z'

[Ang Li @Ang_UCLA]

@artistexyz @soboleffspaces @yudapearl @HL327 @MatheusFacure @causalinf @VC31415 @smueller Just to be clear. We just assumed treat and effect (x and y) to be binary, but not z. The population specific characteristics c and covariates z are not binary.

[Ang Li @Ang_UCLA]

Another intuition why our unit selection is advanced : our objective func is a linear combination of complier,always-taker,never-taker,and defier, while A/B test is a linear combination of (complier+always-taker)(i.e.,treated) and (always-taker+defier)(i.e.,controlled) @yudapearl

[www.ar-tiste.xyz @artistexyz]

@yudapearl @causalinf @VC31415 @soboleffspaces @MatheusFacure
Causal inference, Bayesian Networks, Unit Selection Problem, Uber, CausalML
https://t.co/mMapE3l5OV