| 2025 | AISTATS | Causal Representation Learning from General Environments under Nonparametric Mixing. | Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang |
| 2025 | CogSci | Learning Hidden Causal Factors from Psychometrics Data Using Distributional Information. | Roberto Legaspi, Xinshuai Dong, Donghuo Zeng, Yuewen Sun, Kazushi Ikeda, Peter Spirtes, Kun Zhang |
| 2025 | ICLR | When Selection Meets Intervention: Additional Complexities in Causal Discovery. | Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, Kun Zhang |
| 2025 | ICLR | Prompting Fairness: Integrating Causality to Debias Large Language Models. | Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu |
| 2025 | ICML | Reflection-Window Decoding: Text Generation with Selective Refinement. | Zeyu Tang, Zhenhao Chen, Xiangchen Song, Loka Li, Yunlong Deng, Yifan Shen, Guangyi Chen, Peter Spirtes, Kun Zhang |
| 2025 | ICML | Latent Variable Causal Discovery under Selection Bias. | Haoyue Dai, Yiwen Qiu, Ignavier Ng, Xinshuai Dong, Peter Spirtes, Kun Zhang |
| 2025 | ICML | Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data. | Xinshuai Dong, Ignavier Ng, Boyang Sun, Haoyue Dai, Guang-Yuan Hao, Shunxing Fan, Peter Spirtes, Yumou Qiu, Kun Zhang |
| 2024 | ICLR | Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. | Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang |
| 2024 | ICLR | A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. | Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang |
| 2024 | ICLR | Procedural Fairness Through Decoupling Objectionable Data Generating Components. | Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang |
| 2024 | ICML | Score-Based Causal Discovery of Latent Variable Causal Models. | Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang |
| 2024 | Persuasive | Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome. | Donghuo Zeng, Roberto Sebastian Legaspi, Yuewen Sun, Xinshuai Dong, Kazushi Ikeda, Peter Spirtes, Kun Zhang |
| 2019 | AISTATS | Learning the Structure of a Nonstationary Vector Autoregression. | Daniel Malinsky, Peter Spirtes |
| 2018 | KDD | Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. | Daniel Malinsky, Peter Spirtes |
| 2018 | UAI | Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. | Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour |
| 2013 | AISTATS | Data-driven covariate selection for nonparametric estimation of causal effects. | Doris Entner, Patrik O. Hoyer, Peter Spirtes |
| 2013 | UAI | Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models. | Peter Spirtes |
| 2008 | UAI | Causal discovery of linear acyclic models with arbitrary distributions. | Patrik O. Hoyer, Aapo Hyvrinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu |
| 2008 | UAI | Discovering Cyclic Causal Models by Independent Components Analysis. | Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer |
| 2006 | UAI | A Theoretical Study of Y Structures for Causal Discovery. | Subramani Mani, Gregory F. Cooper, Peter Spirtes |
| 2006 | UAI | Adjacency-Faithfulness and Conservative Causal Inference. | Joseph D. Ramsey, Jiji Zhang, Peter Spirtes |
| 2005 | UAI | Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables. | Ayesha R. Ali, Thomas S. Richardson, Peter Spirtes, Jiji Zhang |
| 2005 | UAI | A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables. | Jiji Zhang, Peter Spirtes |
| 2003 | UAI | Learning Measurement Models for Unobserved Variables. | Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes |
| 2003 | UAI | Strong Faithfulness and Uniform Consistency in Causal Inference. | Jiji Zhang, Peter Spirtes |
| 2001 | AISTATS | An Anytime Algorithm for Causal Inference. | Peter Spirtes |
| 2001 | UAI | Semi-Instrumental Variables: A Test for Instrument Admissibility. | Tianjiao Chu, Richard Scheines, Peter Spirtes |
| 1999 | AISTATS | An experiment in causal discovery using a pneumona database. | Peter Spirtes, Gregory F. Cooper |
| 1997 | AISTATS | A Note on Cyclic Graphs and Dynamical Feedback Systems. | Thomas S. Richardson, Peter Spirtes, Clark Glymour |
| 1997 | AISTATS | A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias. | Peter Spirtes, Thomas S. Richardson |
| 1997 | AISTATS | Heuristic Greedy Search Algorithms for Latent Variable Models. | Peter Spirtes, Thomas S. Richardson, Christopher Meek |
| 1995 | KDD | Learning Bayesian Networks with Discrete Variables from Data. | Peter Spirtes, Christopher Meek |
| 1995 | UAI | Directed Cyclic Graphical Representations of Feedback Models. | Peter Spirtes |
| 1995 | UAI | Causal Inference in the Presence of Latent Variables and Selection Bias. | Peter Spirtes, Christopher Meek, Thomas S. Richardson |
| 1991 | UAI | Detecting Causal Relations in the Presence of Unmeasured Variables. | Peter Spirtes |