A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilis...
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A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable known as the instrument, is a standard technique for learning causal relationships between confounded action, outcome, and context variables. Most recent IV regression algorithms use a two-stage approach, where a deep neural network (DNN) estimator learnt in the first stage is directly plugged into the second stage, in which another DNN is used to estimate the causal effect. Naively plugging the estimator can cause heavy bias in the second stage, especially when regularisation bias is present in the first stage estimator. We propose DML-IV, a non-linear IV regression method that reduces the bias in two-stage IV regressions and effectively learns high-performing policies. We derive a novel learning objective to reduce bias and design the DML-IV algorithm following the double/debiased machine learning (DML) framework. The learnt DML-IV estimator has strong convergence rate and O(N−1/2) suboptimality guarantees that match those when the dataset is unconfounded. DML-IV outperforms state-of-the-art IV regression methods on IV regression benchmarks and learns high-performing policies in the presence of instruments. Copyright 2024 by the author(s)
作者：
Manjunatha, A.S.Venkatramana Bhat, P.
Department of Computer and Communication Engineering India
Department of Computer Science and Engineering India
Data is collected and forwarded to the cluster head by sensor nodes in the Wireless Sensor Network (WSN). Ensuring the confidentiality and integrity of the data that must be provided to the base station is tough. We r...
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Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their enormous parameter size and extremely high requirements for compute power ...
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Water quality prediction methods forecast the short-or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fa...
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作者：
Baba, AbdullatifAlothman, Basil
Computer Science and Engineering Department Kuwait
Computer Engineering Department Ankara Turkey
This paper explores essential aspects of autonomous underwater vehicle (AUV) design, focusing on hull structure, hydrodynamics, propulsion systems, and sensor integration. It also examines the role of underwater Simul...
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