Papers
Abstractions in Causal Models and Game Structures
Sylvia S. Kerkhove, Natasha Alechina, Mehdi Dastani
A Comprehensive Collection of Vignettes for Actual Causation
Christian Odenwald
Composite Graphical Causal Models for Inference on Heterogeneous-Indexed Data
Arne De Temmerman, Mathias Verbeke
Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
Marc Braun, Jose Peña, Adel Daoud
Learning control variables and instruments for causal analysis in observational data
Nicolas Apfel, Martin Huber, Jannis Kueck et al.
Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives
Hao Zhu, Di Zhou, Donna Slonim
Unveiling Causal Calibration: How LLM Scale Influences Statistical Information Interpolation
Markus Englberger, Devendra Singh Dhami
Variance reduction combining pre-experiment and in-experiment data
Zhexiao Lin, Pablo Crespo
Estimating Joint Interventional Distributions from Marginal Interventional Data
Sergio Hernan Garrido Mejia, Elke Kirschbaum, Armin Kekić et al.
Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoders
Martin Huber, Jannis Kueck, Mara Mattes
Causal Importance for Physics-Informed Machine Learning
Daniel Fiifi Tawia Hagan, Thomas Mortier, Cas Decancq et al.
Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms
Katherine Avery, Chinmay Pendse, David Jensen
Learning a Spatial Partitioning and its Causal Relations from Temporal Data
Philippe Brouillard, Sebastien Lachapelle, Julia Kaltenborn et al.
Improving Generative Methods for Causal Evaluation via Simulation-Based Inference
Pracheta Amaranath, Vinitra Muralikrishnan, Amit Sharma et al.
Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases
Federico Baldo, Simon Ferreira, Charles K. Assaad
Automatic debiased machine learning and sensitivity analysis for sample selection models
Jakob Bjelac, Victor Chernozhukov, Phil-Adrian Klotz et al.
Bayesian Hierarchical Invariant Prediction
Francisco Madaleno, Pernille Julie Viuff Sand, Francisco C. Pereira et al.
DAG Learning from Zero-Inflated Count Data Using Continuous Optimization
Noriaki Sato, Marco Scutari, Shuichi Kawano et al.
Causal-ICM: A Data Fusion Framework For Heterogeneous Treatment Effect Estimation With Multi-Task Gaussian Processes
Evangelos Dimitriou, Edwin Fong, Jens Magelund Tarp et al.
Valid Inference for Treatment Effects under Multimodal Confounding
Martin Spindler, Philipp Bach, Victor Chernozhukov et al.
Understanding Task Representations in Neural Networks via Bayesian Ablation
Andrew Joohun Nam, Declan Iain Campbell, Thomas L. Griffiths et al.
A fine-grained look at causal effects in causal spaces
Junhyung Park, Yuqing Zhou