Poster "causal inference" Papers
55 papers found • Page 1 of 2
Conference
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
Harsh Parikh, Trang Nguyen, Elizabeth Stuart et al.
A Counterfactual Semantics for Hybrid Dynamical Systems
Andy Zane, Dmitry Batenkov, Rafal Urbaniak et al.
A Hierarchy of Graphical Models for Counterfactual Inferences
Hongshuo Yang, Elias Bareinboim
Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference
Anpeng Wu, Haiyi Qiu, Zhengming Chen et al.
Causality-guided Prompt Learning for Vision-language Models via Visual Granulation
Mengyu Gao, Qiulei Dong
Causal LLM Routing: End-to-End Regret Minimization from Observational Data
Asterios Tsiourvas, Wei Sun, Georgia Perakis
Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar et al.
Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets
Yuxin Wang, Maresa Schröder, Dennis Frauen et al.
Data Fusion for Partial Identification of Causal Effects
Quinn Lanners, Cynthia Rudin, Alexander Volfovsky et al.
Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
Mathieu Chevalley, Patrick Schwab, Arash Mehrjou
Diverse Influence Component Analysis: A Geometric Approach to Nonlinear Mixture Identifiability
Hoang Son Nguyen, Xiao Fu
Gaussian Mixture Counterfactual Generator
Jong-Hoon Ahn, Akshay Vashist
Handling Missing Responses under Cluster Dependence with Applications to Language Model Evaluation
Zhenghao Zeng, David Arbour, Avi Feller et al.
Incremental Causal Effect for Time to Treatment Initialization
Andrew Ying, Zhichen Zhao, Ronghui Xu
It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
Jikai Jin, Lester Mackey, Vasilis Syrgkanis
Mind Control through Causal Inference: Predicting Clean Images from Poisoned Data
Mengxuan Hu, Zihan Guan, Yi Zeng et al.
Neural Causal Graph for Interpretable and Intervenable Classification
Jiawei Wang, Shaofei Lu, Da Cao et al.
Path-specific effects for pulse-oximetry guided decisions in critical care
Kevin Zhang, Yonghan Jung, Divyat Mahajan et al.
Prediction-Powered Causal Inferences
Riccardo Cadei, Ilker Demirel, Piersilvio De Bartolomeis et al.
Preference Learning for AI Alignment: a Causal Perspective
Katarzyna Kobalczyk, Mihaela van der Schaar
ProDAG: Projected Variational Inference for Directed Acyclic Graphs
Ryan Thompson, Edwin Bonilla, Robert Kohn
Projection Pursuit Density Ratio Estimation
Meilin Wang, Wei Huang, Mingming Gong et al.
PUATE: Efficient ATE Estimation from Treated (Positive) and Unlabeled Units
Masahiro Kato, Fumiaki Kozai, RYO INOKUCHI
Standardizing Structural Causal Models
Weronika Ormaniec, Scott Sussex, Lars Lorch et al.
Stochastic Gradients under Nuisances
Facheng Yu, Ronak Mehta, Alex Luedtke et al.
Treatment Effect Estimation for Optimal Decision-Making
Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal et al.
Turning Sand to Gold: Recycling Data to Bridge On-Policy and Off-Policy Learning via Causal Bound
Tal Fiskus, Uri Shaham
Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization
Xinyan Su, Zhiheng Zhang, Jiyan Qiu et al.
Causal Discovery with Fewer Conditional Independence Tests
Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler
Causal Inference from Competing Treatments
Ana-Andreea Stoica, Vivian Y. Nastl, Moritz Hardt
Causal Inference out of Control: Estimating Performativity without Treatment Randomization
Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner
Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference
Yan Zhong, Xingyu Wu, Li Zhang et al.
Causality Based Front-door Defense Against Backdoor Attack on Language Models
Yiran Liu, Xiaoang Xu, Zhiyi Hou et al.
COIN-Matting: Confounder Intervention for Image Matting
Zhaohe Liao, Jiangtong Li, Jun Lan et al.
Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
Tianyu Guo, Sai Praneeth Karimireddy, Michael Jordan
Continuous Treatment Effects with Surrogate Outcomes
Zhenghao Zeng, David Arbour, Avi Feller et al.
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts
Yuzheng Wang, Dingkang Yang, Zhaoyu Chen et al.
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
Qinshuo Liu, Zixin Wang, Xi'an Li et al.
From Geometry to Causality- Ricci Curvature and the Reliability of Causal Inference on Networks
Amirhossein Farzam, Allen Tannenbaum, Guillermo Sapiro
Graph Neural Network Causal Explanation via Neural Causal Models
Arman Behnam, Binghui Wang
Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach
Darya Biparva, Donatello Materassi
Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
Allen Tran, Aurelien Bibaut, Nathan Kallus
Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects
Aaron Fisher
Language Models Represent Beliefs of Self and Others
Wentao Zhu, Zhining Zhang, Yizhou Wang
Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias
Baohong Li, Haoxuan Li, Ruoxuan Xiong et al.
Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
Md Musfiqur Rahman, Murat Kocaoglu
On Positivity Condition for Causal Inference
Inwoo Hwang, Yesong Choe, Yeahoon Kwon et al.
Position: AI/ML Influencers Have a Place in the Academic Process
Iain Xie Weissburg, Mehir Arora, Xinyi Wang et al.
Position: Is machine learning good or bad for the natural sciences?
David W. Hogg, Soledad Villar
Predictive Coding beyond Correlations
Tommaso Salvatori, Luca Pinchetti, Amine M'Charrak et al.