TLRNet: Estimating Individual Treatment Effect based on Local Information and Single Learner Structure

arXiv:2607.22762 · stat.ML, cs.LG · Submitted 2026-07-23 · Read on arXiv

Ali Haghpanah Jahromi, Mohammad Taheri, Zohreh Azimifar

stat.ML, cs.LG

Submitted: 2026-07-23

Comments: 7 pages, 1 figure, 2 tables. This is the author-accepted manuscript of the paper presented at the 2nd International Conference on Artificial Intelligence and Software Engineering (AI-SOFT 2024), Shiraz University, Iran

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Causal Inference — 16× in this paper · explained in 5 episodes
• Loss Function — 6× in this paper · explained in 2 episodes
• Meta-learning — 5× in this paper · explained in 3 episodes
• Representation Learning — 5× in this paper · explained in 3 episodes
• Objective Function — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Selection Bias — 4× in this paper · explained in 5 episodes
• Average Treatment Effect — 3× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Doubly Robust — 3× in this paper
• Bayesian Additive Regression Trees — 2× in this paper
• Generalization — 2× in this paper · explained in 14 episodes
• Generalization Bounds — 2× in this paper · explained in 2 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Conditional Average Treatment Effect — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Dimensionality Reduction — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Infant Health and Development Program — 1× in this paper

Sources

• Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark

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