no code implementations • 21 Apr 2024 • Donghuo Zeng, Roberto S. Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang
In this paper, we present a novel approach that tracks a user's latent personality dimensions (LPDs) during ongoing persuasion conversation and generates tailored counterfactual utterances based on these LPDs to optimize the overall persuasion outcome.
1 code implementation • 21 Mar 2024 • Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang
This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts.
no code implementations • 13 Mar 2024 • Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu
Large language models (LLMs) can easily generate biased and discriminative responses.
no code implementations • 18 Dec 2023 • Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.
1 code implementation • 5 Nov 2023 • Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang
We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i. e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals.
1 code implementation • 31 Jul 2023 • Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.
no code implementations • 20 Oct 2022 • Haoyue Dai, Peter Spirtes, Kun Zhang
Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error.
no code implementations • 18 Jul 2022 • Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes
The m-connecting imset and factorization criterion provide two new statistical tools for learning and inference with ADMG models.
1 code implementation • 25 Feb 2022 • Anne Helby Petersen, Joseph Ramsey, Claus Thorn Ekstrøm, Peter Spirtes
We use random subsampling to investigate real data performance on small samples and again find that SLdisco is less sensitive towards sample size and hence seems to better utilize the information available in small datasets.
no code implementations • 3 Jul 2021 • Shuyan Wang, Peter Spirtes
Kalisch and B\"{u}hlmann (2007) showed that for linear Gaussian models, under the Causal Markov Assumption, the Strong Causal Faithfulness Assumption, and the assumption of causal sufficiency, the PC algorithm is a uniformly consistent estimator of the Markov Equivalence Class of the true causal DAG for linear Gaussian models; it follows from this that for the identifiable causal effects in the Markov Equivalence Class, there are uniformly consistent estimators of causal effects as well.
no code implementations • 2 Mar 2020 • Naji Shajarisales, Peter Spirtes, Kun Zhang
Yet the annotation process is an important part of the data collection, and in many cases it naturally depends on certain features of the data (e. g., the intensity of an image and the size of the object to be detected in the image).
no code implementations • 10 Jun 2017 • Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour
This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance.
1 code implementation • 9 Apr 2017 • Andrew J Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data.
no code implementations • 11 May 2012 • Peter Grunwald, Peter Spirtes
This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11 2010.
no code implementations • NeurIPS 2009 • Arthur Gretton, Peter Spirtes, Robert E. Tillman
This results in a more computationally efficient approach that is useful for arbitrary distributions even when additive noise models are invertible.