v1v2v3 (latest)

Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data

29 March 2022

ArXiv (abs)PDF HTML Github (2★)

Papers citing "Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data"

23 / 23 papers shown

Title
Do-PFN: In-Context Learning for Causal Effect Estimation Jake Robertson Arik Reuter Siyuan Guo Noah Hollmann Frank Hutter Bernhard Schölkopf CML 63 0 0 06 Jun 2025
Counterfactual reasoning: an analysis of in-context emergence Moritz Miller Bernhard Schölkopf Siyuan Guo ReLM LRM 159 0 0 05 Jun 2025
Bayesian Hierarchical Invariant Prediction Francisco Madaleno Pernille Julie Viuff Sand Francisco C. Pereira Sergio Hernan Garrido Mejia 74 0 0 16 May 2025
Multi-Domain Causal Discovery in Bijective Causal Models Kasra Jalaldoust Saber Salehkaleybar Negar Kiyavash CML 98 0 0 30 Apr 2025
Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms R. Karlsson Jesse H. Krijthe CML 155 1 0 10 Feb 2025
Causal vs. Anticausal merging of predictors Sergio Hernan Garrido Mejia Patrick Blobaum Bernhard Schölkopf Dominik Janzing 68 0 0 14 Jan 2025
Detecting and Measuring Confounding Using Causal Mechanism Shifts Abbavaram Gowtham Reddy Vineeth N Balasubramanian CML 78 2 0 26 Sep 2024
Do Finetti: On Causal Effects for Exchangeable Data Siyuan Guo Chi Zhang Karthika Mohan Ferenc Huszár Bernhard Schölkopf 71 5 0 29 May 2024
Position: Understanding LLMs Requires More Than Statistical Generalization Patrik Reizinger Szilvia Ujváry Anna Mészáros A. Kerekes Wieland Brendel Ferenc Huszár 130 16 0 03 May 2024
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis Goutham Rajendran Patrik Reizinger Wieland Brendel Pradeep Ravikumar CML 156 8 0 29 Nov 2023
Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI S. Du Yaxiu Sun Changying Du 31 0 0 28 Aug 2023
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity H. Aliee Ferdinand Kapl Soroor Hediyeh-zadeh Fabian J. Theis CML 94 7 0 02 Jul 2023
Bivariate Causal Discovery using Bayesian Model Selection Anish Dhir Samuel Power Mark van der Wilk CML 63 3 0 05 Jun 2023
Provably Learning Object-Centric Representations Jack Brady Roland S. Zimmermann Yash Sharma Bernhard Schölkopf Julius von Kügelgen Wieland Brendel OCL 89 36 0 23 May 2023
Out-of-Variable Generalization for Discriminative Models Siyuan Guo J. Wildberger Bernhard Schölkopf OOD CML 90 2 0 16 Apr 2023
Dataflow graphs as complete causal graphs Andrei Paleyes Siyuan Guo Bernhard Schölkopf Neil D. Lawrence 63 7 0 16 Mar 2023
AI for Science: An Emerging Agenda Philipp Berens Kyle Cranmer Neil D. Lawrence U. V. Luxburg Jessica Montgomery 65 6 0 07 Mar 2023
On the Interventional Kullback-Leibler Divergence J. Wildberger Siyuan Guo Arnab Bhattacharyya Bernhard Schölkopf OOD CML 79 6 0 10 Feb 2023
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics P. Stinis C. Daskalakis P. Atzberger SyDa GAN 83 5 0 07 Feb 2023
Non-parametric identifiability and sensitivity analysis of synthetic control models Jakob Zeitler Athanasios Vlontzos Ciarán M. Gilligan-Lee CML 57 6 0 18 Jan 2023
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation Michael Chang Thomas Griffiths Sergey Levine OCL 114 61 0 02 Jul 2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis Ronan Perry Julius von Kügelgen Bernhard Schölkopf 100 50 0 04 Jun 2022
Detecting hidden confounding in observational data using multiple environments R. Karlsson Jesse H. Krijthe CML OOD 94 13 0 27 May 2022