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Bibliografia.bib
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@article{Wager2018,
title={Estimation and inference of heterogeneous treatment effects using random forests},
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year={2018},
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}
@article{Athey2019,
title={Estimating treatment effects with causal forests: An application},
author={Athey, Susan and Wager, Stefan},
journal={arXiv preprint arXiv:1902.07409},
year={2019}
}
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title={Random forests},
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@article{Athey2019b,
title={Generalized random forests},
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}
@misc{singh2020debiased,
title={De-biased Machine Learning in Instrumental Variable Models for Treatment Effects},
author={Rahul Singh and Liyang Sun},
year={2020},
eprint={1909.05244},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
@article{Rubin1974,
title={Estimating causal effects of treatments in randomized and nonrandomized studies.},
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title={Using causal forests to predict treatment heterogeneity: An application to summer jobs},
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year={2017}
}
@article{suk2020random,
title={Random forests approach for causal inference with clustered observational data},
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year={2020},
publisher={Taylor \& Francis}
}
@article{athey2019estimating,
title={Estimating treatment effects with causal forests: An application},
author={Athey, Susan and Wager, Stefan},
journal={arXiv preprint arXiv:1902.07409},
year={2019}
}
@article{wager2018estimation,
title={Estimation and inference of heterogeneous treatment effects using random forests},
author={Wager, Stefan and Athey, Susan},
journal={Journal of the American Statistical Association},
volume={113},
number={523},
pages={1228--1242},
year={2018},
publisher={Taylor \& Francis}
}
@article{lechner2018modified,
title={Modified causal forests for estimating heterogeneous causal effects},
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journal={arXiv preprint arXiv:1812.09487},
year={2018}
}
@article{breiman2001random,
title={Random forests},
author={Breiman, Leo},
journal={Machine learning},
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@inproceedings{oprescu2019orthogonal,
title={Orthogonal random forest for causal inference},
author={Oprescu, Miruna and Syrgkanis, Vasilis and Wu, Zhiwei Steven},
booktitle={International Conference on Machine Learning},
pages={4932--4941},
year={2019},
organization={PMLR}
}
@article{Chernozhukov2018,
author = {Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Duflo, Esther and Hansen, Christian and Newey, Whitney and Robins, James},
title = "{Double/debiased machine learning for treatment and structural parameters}",
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number = {1},
pages = {C1-C68},
year = {2018},
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doi = {10.1111/ectj.12097},
}
@techreport{varian2018artificial,
title={Artificial intelligence, economics, and industrial organization},
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year={2018},
institution={National Bureau of Economic Research}
}
@article{athey2019machine,
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}
@article{andini2018targeting,
title={Targeting with machine learning: An application to a tax rebate program in Italy},
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@article{sokolov2016economic,
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@misc{Syrgkanis2019,
title={Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments},
author={Vasilis Syrgkanis and Victor Lei and Miruna Oprescu and Maggie Hei and Keith Battocchi and Greg Lewis},
year={2019},
eprint={1905.10176},
archivePrefix={arXiv},
primaryClass={econ.EM}
}
@InProceedings{Oprescu19a,
title = {Orthogonal Random Forest for Causal Inference},
author = {Oprescu, Miruna and Syrgkanis, Vasilis and Wu, Zhiwei Steven},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {4932--4941},
year = {2019},
editor = {Kamalika Chaudhuri and Ruslan Salakhutdinov},
volume = {97},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
url = {http://proceedings.mlr.press/v97/oprescu19a.html}
}