Papers
132 papers found
Fundamental Properties of Causal Entropy and Information Gain
Francisco N. F. Q. Simoes, Mehdi Dastani, Thijs van Ommen
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
Philipp Bach, Oliver Schacht, Victor Chernozhukov et al.
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
Simon Bing, Urmi Ninad, Jonas Wahl et al.
Implicit and Explicit Policy Constraints for Offline Reinforcement Learning
Yang Liu, Marius Hofert
Inference of nonlinear causal effects with application to TWAS with GWAS summary data
Ben Dai, Chunlin Li, Haoran Xue et al.
Lifted Causal Inference in Relational Domains
Malte Luttermann, Mattis Hartwig, Tanya Braun et al.
Meaningful Causal Aggregation and Paradoxical Confounding
Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler et al.
On the Identifiability of Quantized Factors
Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien et al.
On the Impact of Neighbourhood Sampling to Satisfy Sufficiency and Necessity Criteria in Explainable AI
Urja Pawar, Christian Beder, Ruairi O\textsc\char13Reilly et al.
On the Lasso for Graphical Continuous Lyapunov Models
Philipp Dettling, Mathias Drton, Mladen Kolar
Pragmatic Fairness: Developing Policies with Outcome Disparity Control
Limor Gultchin, Siyuan Guo, Alan Malek et al.
Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice
Damian Machlanski, Spyridon Samothrakis, Paul S Clarke
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models
Thong Pham, Shohei Shimizu, Hideitsu Hino et al.
Semiparametric Efficient Inference in Adaptive Experiments
Thomas Cook, Alan Mishler, Aaditya Ramdas
Sequential Deconfounding for Causal Inference with Unobserved Confounders
Tobias Hatt, Stefan Feuerriegel
Structure Learning with Continuous Optimization: A Sober Look and Beyond
Ignavier Ng, Biwei Huang, Kun Zhang
The PetShop Dataset — Finding Causes of Performance Issues across Microservices
Michaela Hardt, William Roy Orchard, Patrick Blöbaum et al.
Towards the Reusability and Compositionality of Causal Representations
Davide Talon, Phillip Lippe, Stuart James et al.
Toward the Identifiability of Comparative Deep Generative Models
Romain Lopez, Jan-Christian Huetter, Ehsan Hajiramezanali et al.
A Meta-Reinforcement Learning Algorithm for Causal Discovery
Andreas W.M. Sauter, Erman Acar, Vincent Francois-Lavet
An Algorithm and Complexity Results for Causal Unit Selection
Haiying Huang, Adnan Darwiche
Backtracking Counterfactuals
Julius Von Kügelgen, Abdirisak Mohamed, Sander Beckers
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding
Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes et al.
Branch-Price-and-Cut for Causal Discovery
James Cussens