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Found 16 papers, 9 papers with code
General Information Metrics for Improving AI Model Training Efficiency
To address the growing size of AI model training data and the lack of a universal data selection methodology-factors that significantly drive up training costs -- this paper presents the General Information Metrics Evaluation (GIME) method.
Causality for Large Language Models
How to embed causality into the training process of LLMs and build more general and intelligent models remains unexplored.
Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression
In causal inference, encouragement designs (EDs) are widely used to analyze causal effects, when randomized controlled trials (RCTs) are impractical or compliance to treatment cannot be perfectly enforced.
Causal Inference with Complex Treatments: A Survey
Causal inference plays an important role in explanatory analysis and decision making across various fields like statistics, marketing, health care, and education.
Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations
Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups.
Learning Discrete Latent Variable Structures with Tensor Rank Conditions
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns.
Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science.
Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other.
Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series
Learning directed acyclic graphs (DAGs) to identify causal relations underlying observational data is crucial but also poses significant challenges.
Instrumental Variables in Causal Inference and Machine Learning: A Survey
This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning.
Confounder Balancing for Instrumental Variable Regression with Latent Variable
This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation.
Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation
The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.
Treatment effect estimation with confounder balanced instrumental variable regression
In this paper, we propose a Confounder Balanced IV Regression (CB-IV) algorithm to jointly remove the bias from the unmeasured confounders with IV regression and reduce the bias from the observed confounders by balancing for treatment effect estimation.
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome.
Learning Decomposed Representation for Counterfactual Inference
The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing.
