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Peter Spirtes

26 papers · 2006–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (19) 🌍 Conference Polyglot (7) πŸ—ΊοΈ Taxonomy Completionist (20) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🀝 Dynamic Duo (14) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (13) 🧬 Topic Evolution πŸ† Keyword Champion πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (67) ⚑ Prolific Year (7) πŸ’Ž Century Club (26) πŸ”₯ Unstoppable (5) πŸ“ˆ Trend Setter

Conferences

AISTATS (6) ICLR (5) ICML (4) NIPS (4) JMLR (3) CLEAR (2) PGM (2)

Top co-authors

Kun Zhang (14) Xinshuai Dong (8) Ignavier Ng (8) Haoyue Dai (6) Biwei Huang (5) Zeyu Tang (4) Roberto Legaspi (3) Songyao Jin (3) Yang Liu (2) Yujia Zheng (2)

Keywords

causal inference (6) causal discovery (6) graphical model (5) latent variable (4) directed acyclic graph (3) structure learning (3) causal structure learning (2) linear non-gaussian model (2) causal effect estimation (2) ancestral graph (2) observational datum (2) causal effect (2) nonlinear structure learning (1) regression analysis (1) independence testing (1) conditional dependence (1) graph structure (1) causal structure (1) latent variable model (1) nonparametric estimation (1)

Papers

Latent Variable Causal Discovery under Selection Bias ICML 2025
Reflection-Window Decoding: Text Generation with Selective Refinement ICML 2025
Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data ICML 2025
Causal Representation Learning from General Environments under Nonparametric Mixing AISTATS 2025
Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models CLEAR 2025
Prompting Fairness: Integrating Causality to Debias Large Language Models ICLR 2025
When Selection Meets Intervention: Additional Complexities in Causal Discovery ICLR 2025
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning NIPS 2024
Causal-learn: Causal Discovery in Python JMLR 2024
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View ICLR 2024
Procedural Fairness Through Decoupling Objectionable Data Generating Components ICLR 2024
Score-Based Causal Discovery of Latent Variable Causal Models ICML 2024
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables ICLR 2024
On the Parameter Identifiability of Partially Observed Linear Causal Models NIPS 2024
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models NIPS 2022
A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the $k$-Triangle-Faithfulness Assumption CLEAR 2022
On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge AISTATS 2020
Learning the Structure of a Nonstationary Vector Autoregression AISTATS 2019
A Hybrid Causal Search Algorithm for Latent Variable Models PGM 2016
Estimating Causal Effects with Ancestral Graph Markov Models PGM 2016
Data-driven covariate selection for nonparametric estimation of causal effects AISTATS 2013
Statistical test for consistent estimation of causal effects in linear non-Gaussian models AISTATS 2012
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables AISTATS 2011
Introduction to Causal Inference JMLR 2010
Nonlinear directed acyclic structure learning with weakly additive noise models NIPS 2009
Learning the Structure of Linear Latent Variable Models JMLR 2006