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← Core AI
Artificial Intelligence
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Core AI
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Causal Inference
3,310 papers
Papers per year
2002: 1
2003: 2
2004: 1
2005: 2
2006: 7
2007: 6
2008: 9
2009: 9
2010: 15
2011: 8
2012: 15
2013: 32
2014: 16
2015: 28
2016: 39
2017: 62
2018: 98
2019: 201
2020: 277
2021: 372
2022: 426
2023: 511
2024: 601
2025: 389
2026: 183
Papers
A Logic-based Approach to Contrastive Explainability for Neurosymbolic Visual Question Answering
IJCAI 2023
Explainable Reinforcement Learning via a Causal World Model
IJCAI 2023
Causal Deep Reinforcement Learning Using Observational Data
IJCAI 2023
Formal Explanations of Neural Network Policies for Planning
IJCAI 2023
Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences
IJCAI 2023
Quantifying Consistency and Information Loss for Causal Abstraction Learning
IJCAI 2023
Learning Causal Effects on Hypergraphs (Extended Abstract)
IJCAI 2023
Anti-unification and Generalization: A Survey
IJCAI 2023
Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract)
IJCAI 2023
Argumentation for Interactive Causal Discovery
IJCAI 2023
Tonal coarticulation as a cue for upcoming prosodic boundary
INTERSPEECH 2023
Automatic speaker recognition with variation across vocal conditions: a controlled experiment with implications for forensics
INTERSPEECH 2023
Python package for causal discovery based on LiNGAM
JMLR 2023
The d-Separation Criterion in Categorical Probability
JMLR 2023
Inference for a Large Directed Acyclic Graph with Unspecified Interventions
JMLR 2023
Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
JMLR 2023
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
JMLR 2023
Jump Interval-Learning for Individualized Decision Making with Continuous Treatments
JMLR 2023
Evaluating Instrument Validity using the Principle of Independent Mechanisms
JMLR 2023
Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding
JMLR 2023
Clustering and Structural Robustness in Causal Diagrams
JMLR 2023
Model-based Causal Discovery for Zero-Inflated Count Data
JMLR 2023
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
JMLR 2023
Scalable Computation of Causal Bounds
JMLR 2023
Causal Discovery with Unobserved Confounding and Non-Gaussian Data
JMLR 2023
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