Papers
132 papers found
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister et al.
Causal Abstraction with Soft Interventions
Riccardo Massidda, Atticus Geiger, Thomas Icard et al.
Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling
Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi et al.
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco et al.
Causal Inference Despite Limited Global Confounding via Mixture Models
Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani et al.
Causal Inference under Interference and Model Uncertainty
Chi Zhang, Karthika Mohan, Judea Pearl
Causal Learning through Deliberate Undersampling
Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani et al.
Causal Models with Constraints
Sander Beckers, Joseph Halpern, Christopher Hitchcock
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Yuejiang Liu, Alexandre Alahi, Chris Russell et al.
Directed Graphical Models and Causal Discovery for Zero-Inflated Data
Shiqing Yu, Mathias Drton, Ali Shojaie
Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model
Mario A. T. Figueiredo, Catarina Oliveira
Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios
Luca Castri, Sariah Mghames, Marc Hanheide et al.
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding
Graham Van Goffrier, Lucas Maystre, Ciarán Mark Gilligan-Lee
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
Rhys Peter Matthew Howard, Lars Kunze
Factual Observation Based Heterogeneity Learning for Counterfactual Prediction
Hao Zou, Haotian Wang, Renzhe Xu et al.
Generalizing Clinical Trials with Convex Hulls
Eric Strobl, Thomas A Lasko
Image-based Treatment Effect Heterogeneity
Connor Thomas Jerzak, Fredrik Daniel Johansson, Adel Daoud
Influence-Aware Attention for Multivariate Temporal Point Processes
Xiao Shou, Tian Gao, Dharmashankar Subramanian et al.
Instrumental Processes Using Integrated Covariances
Søren Wengel Mogensen
Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions
Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei et al.
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Romain Lopez, Natasa Tagasovska, Stephen Ra et al.
Learning Conditional Granger Causal Temporal Networks
Ananth Balashankar, Srikanth Jagabathula, Lakshmi Subramanian
Leveraging Causal Graphs for Blocking in Randomized Experiments
Abhishek Kumar Umrawal
Local Causal Discovery for Estimating Causal Effects
Shantanu Gupta, David Childers, Zachary Chase Lipton