CLeaR 2023

39 papers

Sample-Specific Root Causal Inference with Latent Variables

Eric Strobl, Thomas A Lasko

Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling

Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani

Causal Models with Constraints

Sander Beckers, Joseph Halpern, Christopher Hitchcock

Non-parametric identifiability and sensitivity analysis of synthetic control models

Jakob Zeitler, Athanasios Vlontzos, Ciarán Mark Gilligan-Lee

Factorization of the Partial Covariance in Singly-Connected Path Diagrams

Jose Peña

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

Local Dependence Graphs for Discrete Time Processes

Wojciech Niemiro, Łukasz Rajkowski

Scalable Causal Discovery with Score Matching

Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise

Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding

Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen

Learning Conditional Granger Causal Temporal Networks

Ananth Balashankar, Srikanth Jagabathula, Lakshmi Subramanian

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling

Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan Pritchard

Branch-Price-and-Cut for Causal Discovery

James Cussens

Instrumental Processes Using Integrated Covariances

Søren Wengel Mogensen

A Meta-Reinforcement Learning Algorithm for Causal Discovery

Andreas W.M. Sauter, Erman Acar, Vincent Francois-Lavet

Causal Inference Despite Limited Global Confounding via Mixture Models

Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard Schulman

Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning

Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow

Image-based Treatment Effect Heterogeneity

Connor Thomas Jerzak, Fredrik Daniel Johansson, Adel Daoud

Causal Learning through Deliberate Undersampling

Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey Plis

Influence-Aware Attention for Multivariate Temporal Point Processes

Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin Bennett

Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour

Rhys Peter Matthew Howard, Lars Kunze

On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition

Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-tak Zhang, Sanghack Lee

Local Causal Discovery for Estimating Causal Effects

Shantanu Gupta, David Childers, Zachary Chase Lipton

Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?

Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal

Causal Inference under Interference and Model Uncertainty

Chi Zhang, Karthika Mohan, Judea Pearl

Factual Observation Based Heterogeneity Learning for Counterfactual Prediction

Hao Zou, Haotian Wang, Renzhe Xu, Bo Li, Jian Pei

On the Interventional Kullback-Leibler Divergence

Jonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf

Unsupervised Object Learning via Common Fate

Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler

Practical Algorithms for Orientations of Partially Directed Graphical Models

Malte Luttermann, Marcel Wienöbst, Maciej Liśkiewicz

Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios

Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto

Leveraging Causal Graphs for Blocking in Randomized Experiments

Abhishek Kumar Umrawal

Generalizing Clinical Trials with Convex Hulls

Eric Strobl, Thomas A Lasko

Backtracking Counterfactuals

Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers

Stochastic Causal Programming for Bounding Treatment Effects

Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva

Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model

Mario A. T. Figueiredo, Catarina Oliveira

Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions

Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei, W. Dhammika Widanage, Theodoros Damoulas

Causal Abstraction with Soft Interventions

Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu

Directed Graphical Models and Causal Discovery for Zero-Inflated Data

Shiqing Yu, Mathias Drton, Ali Shojaie

An Algorithm and Complexity Results for Causal Unit Selection

Haiying Huang, Adnan Darwiche