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Found 7 papers, 5 papers with code
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.
