Learning Decision Policies with Instrumental Variables through Double Machine Learning ...
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...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2405.08498 https://arxiv.org/abs/2405.08498 |
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ftdatacite:10.48550/arxiv.2405.08498 2024-09-15T18:03:49+00:00 Learning Decision Policies with Instrumental Variables through Double Machine Learning ... Shao, Daqian Soleymani, Ashkan Quinzan, Francesco Kwiatkowska, Marta 2024 https://dx.doi.org/10.48550/arxiv.2405.08498 https://arxiv.org/abs/2405.08498 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Machine Learning cs.LG Machine Learning stat.ML FOS Computer and information sciences article Article Preprint CreativeWork 2024 ftdatacite https://doi.org/10.48550/arxiv.2405.08498 2024-08-01T08:52:50Z 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 ... : Accepted at ICML 2024 ... Report DML DataCite |
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Machine Learning cs.LG Machine Learning stat.ML FOS Computer and information sciences |
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Machine Learning cs.LG Machine Learning stat.ML FOS Computer and information sciences Shao, Daqian Soleymani, Ashkan Quinzan, Francesco Kwiatkowska, Marta Learning Decision Policies with Instrumental Variables through Double Machine Learning ... |
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Machine Learning cs.LG Machine Learning stat.ML FOS Computer and information sciences |
description |
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 ... : Accepted at ICML 2024 ... |
format |
Report |
author |
Shao, Daqian Soleymani, Ashkan Quinzan, Francesco Kwiatkowska, Marta |
author_facet |
Shao, Daqian Soleymani, Ashkan Quinzan, Francesco Kwiatkowska, Marta |
author_sort |
Shao, Daqian |
title |
Learning Decision Policies with Instrumental Variables through Double Machine Learning ... |
title_short |
Learning Decision Policies with Instrumental Variables through Double Machine Learning ... |
title_full |
Learning Decision Policies with Instrumental Variables through Double Machine Learning ... |
title_fullStr |
Learning Decision Policies with Instrumental Variables through Double Machine Learning ... |
title_full_unstemmed |
Learning Decision Policies with Instrumental Variables through Double Machine Learning ... |
title_sort |
learning decision policies with instrumental variables through double machine learning ... |
publisher |
arXiv |
publishDate |
2024 |
url |
https://dx.doi.org/10.48550/arxiv.2405.08498 https://arxiv.org/abs/2405.08498 |
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DML |
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DML |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2405.08498 |
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1810441277882236928 |