Bibliografia.bib

@article{Fellner2013,
  title={Testing enforcement strategies in the field: Threat, moral appeal and social information},
  author={Fellner, Gerlinde and Sausgruber, Rupert and Traxler, Christian},
  journal={Journal of the European Economic Association},
  volume={11},
  number={3},
  pages={634--660},
  year={2013},
  publisher={Oxford University Press}
}

@article{Ferman2019,
  title={A simple way to assess inference methods},
  author={Ferman, Bruno},
  journal={arXiv preprint arXiv:1912.08772},
  year={2019}
}

@article{Dwenger2016,
  title={Extrinsic and intrinsic motivations for tax compliance: Evidence from a field experiment in Germany},
  author={Dwenger, Nadja and Kleven, Henrik and Rasul, Imran and Rincke, Johannes},
  journal={American Economic Journal: Economic Policy},
  volume={8},
  number={3},
  pages={203--32},
  year={2016}
}

@article{Luttmer2014,
  title={Tax morale},
  author={Luttmer, Erzo FP and Singhal, Monica},
  journal={Journal of economic perspectives},
  volume={28},
  number={4},
  pages={149--68},
  year={2014}
}

@article{Naritomi2019,
  title={Consumers as tax auditors},
  author={Naritomi, Joana},
  journal={American Economic Review},
  volume={109},
  number={9},
  pages={3031--72},
  year={2019}
}

@article{Duflo2007,
  title={Using randomization in development economics research: A toolkit},
  author={Duflo, Esther and Glennerster, Rachel and Kremer, Michael},
  journal={Handbook of development economics},
  volume={4},
  pages={3895--3962},
  year={2007},
  publisher={Elsevier}
}

@article{Ai2003,
  title={Interaction terms in logit and probit models},
  author={Ai, Chunrong and Norton, Edward C},
  journal={Economics letters},
  volume={80},
  number={1},
  pages={123--129},
  year={2003},
  publisher={Elsevier}
}

@book{Imbens2015,
  title={Causal inference in statistics, social, and biomedical sciences},
  author={Imbens, Guido W and Rubin, Donald B},
  year={2015},
  publisher={Cambridge University Press}
}

@article{Bowles2001,
Author = {Bowles, Samuel and Gintis, Herbert and Osborne, Melissa},
Title = {The Determinants of Earnings: A Behavioral Approach},
Journal = {Journal of Economic Literature},
Volume = {39},
Number = {4},
Year = {2001},
Month = {December},
Pages = {1137-1176},
DOI = {10.1257/jel.39.4.1137},
URL = {https://www.aeaweb.org/articles?id=10.1257/jel.39.4.1137}}

@book{Ashenfelter2010,
  title={Handbook of labor economics},
  author={Ashenfelter, Orley and Card, David},
  year={2010},
  publisher={Elsevier}
}

@article{Imbens2015b,
  title={Matching methods in practice: Three examples},
  author={Imbens, Guido W},
  journal={Journal of Human Resources},
  volume={50},
  number={2},
  pages={373--419},
  year={2015},
  publisher={University of Wisconsin Press}
}

@book{Angrist2008,
  title={Mostly harmless econometrics: An empiricist's companion},
  author={Angrist, Joshua D and Pischke, J{\"o}rn-Steffen},
  year={2008},
  publisher={Princeton university press}
}

@article{Belloni2014,
  title={Inference on treatment effects after selection among high-dimensional controls},
  author={Belloni, Alexandre and Chernozhukov, Victor and Hansen, Christian},
  journal={The Review of Economic Studies},
  volume={81},
  number={2},
  pages={608--650},
  year={2014},
  publisher={Oxford University Press}
}

@article{Wager2018,
  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{Athey2019,
  title={Estimating treatment effects with causal forests: An application},
  author={Athey, Susan and Wager, Stefan},
  journal={arXiv preprint arXiv:1902.07409},
  year={2019}
}

@article{Breiman2001,
  title={Random forests},
  author={Breiman, Leo},
  journal={Machine learning},
  volume={45},
  number={1},
  pages={5--32},
  year={2001},
  publisher={Springer}
}

@article{Athey2019b,
  title={Generalized random forests},
  author={Athey, Susan and Tibshirani, Julie and Wager, Stefan and others},
  journal={The Annals of Statistics},
  volume={47},
  number={2},
  pages={1148--1178},
  year={2019},
  publisher={Institute of Mathematical Statistics}
}

@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.},
  author={Rubin, Donald B},
  journal={Journal of educational Psychology},
  volume={66},
  number={5},
  pages={688},
  year={1974},
  publisher={American Psychological Association}
}

@article{davis2017using,
  title={Using causal forests to predict treatment heterogeneity: An application to summer jobs},
  author={Davis, Jonathan and Heller, Sara B},
  journal={American Economic Review},
  volume={107},
  number={5},
  pages={546--50},
  year={2017}
}

@article{suk2020random,
  title={Random forests approach for causal inference with clustered observational data},
  author={Suk, Youmi and Kang, Hyunseung and Kim, Jee-Seon},
  journal={Multivariate Behavioral Research},
  pages={1--24},
  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},
  author={Lechner, Michael},
  journal={arXiv preprint arXiv:1812.09487},
  year={2018}
}

@article{breiman2001random,
  title={Random forests},
  author={Breiman, Leo},
  journal={Machine learning},
  volume={45},
  number={1},
  pages={5--32},
  year={2001},
  publisher={Springer}
}

@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}",
    journal = {The Econometrics Journal},
    volume = {21},
    number = {1},
    pages = {C1-C68},
    year = {2018},
    month = {01},
    doi = {10.1111/ectj.12097},
}

@techreport{varian2018artificial,
  title={Artificial intelligence, economics, and industrial organization},
  author={Varian, Hal},
  year={2018},
  institution={National Bureau of Economic Research}
}

@article{athey2019machine,
  title={Machine learning methods that economists should know about},
  author={Athey, Susan and Imbens, Guido W},
  journal={Annual Review of Economics},
  volume={11},
  pages={685--725},
  year={2019},
  publisher={Annual Reviews}
}

@article{andini2018targeting,
  title={Targeting with machine learning: An application to a tax rebate program in Italy},
  author={Andini, Monica and Ciani, Emanuele and de Blasio, Guido and D'Ignazio, Alessio and Salvestrini, Viola},
  journal={Journal of Economic Behavior \& Organization},
  volume={156},
  pages={86--102},
  year={2018},
  publisher={Elsevier}
}

@inproceedings{shao2002applying,
  title={Applying data mining to detect fraud behavior in customs declaration},
  author={Shao, Hua and Zhao, Hong and Chang, Gui-Ran},
  booktitle={Proceedings. International Conference on Machine Learning and Cybernetics},
  volume={3},
  pages={1241--1244},
  year={2002},
  organization={IEEE}
}

@article{sokolov2016economic,
  title={Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters},
  author={Sokolov-Mladenovi{\'c}, Svetlana and Milovan{\v{c}}evi{\'c}, Milos and Mladenovi{\'c}, Igor and Alizamir, Meysam},
  journal={Computers in Human Behavior},
  volume={65},
  pages={43--45},
  year={2016},
  publisher={Elsevier}
}

@techreport{goldfarb2018ai,
  title={AI and international trade},
  author={Goldfarb, Avi and Trefler, Daniel},
  year={2018},
  institution={National Bureau of Economic Research}
}

@inproceedings{de2018tax,
  title={Tax fraud detection for under-reporting declarations using an unsupervised machine learning approach},
  author={de Roux, Daniel and Perez, Boris and Moreno, Andr{\'e}s and Villamil, Maria del Pilar and Figueroa, C{\'e}sar},
  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={215--222},
  year={2018}
}

@article{gonzalez2013characterization,
  title={Characterization and detection of taxpayers with false invoices using data mining techniques},
  author={Gonz{\'a}lez, Pamela Castell{\'o}n and Vel{\'a}squez, Juan D},
  journal={Expert Systems with Applications},
  volume={40},
  number={5},
  pages={1427--1436},
  year={2013},
  publisher={Elsevier}
}

@inproceedings{adamov2019machine,
  title={Machine Learning and Advanced Analytics in Tax Fraud Detection},
  author={Adamov, Abzetdin Z},
  booktitle={2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT)},
  pages={1--5},
  year={2019},
  organization={IEEE}
}

@inproceedings{paula2016deep,
  title={Deep learning anomaly detection as support fraud investigation in brazilian exports and anti-money laundering},
  author={Paula, Ebberth L and Ladeira, Marcelo and Carvalho, Rommel N and Marzagao, Thiago},
  booktitle={2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  pages={954--960},
  year={2016},
  organization={IEEE}
}

@incollection{athey2018impact,
  title={The impact of machine learning on economics},
  author={Athey, Susan},
  booktitle={The economics of artificial intelligence: An agenda},
  pages={507--547},
  year={2018},
  publisher={University of Chicago Press}
}

@incollection{lederman2018comment,
  title={Comment on" The Impact of Machine Learning on Economics"},
  author={Lederman, Mara},
  booktitle={The Economics of Artificial Intelligence: An Agenda},
  pages={548--551},
  year={2018},
  publisher={University of Chicago Press}
}

@article{judge2019combining,
  title={Combining the Information From Econometrics Learning (EL) and Machine Learning (ML)},
  author={Judge, George},
  journal={Available at SSRN 3376953},
  year={2019}
}

@article{athey2017state,
  title={The state of applied econometrics: Causality and policy evaluation},
  author={Athey, Susan and Imbens, Guido W},
  journal={Journal of Economic Perspectives},
  volume={31},
  number={2},
  pages={3--32},
  year={2017}
}

@article{mullainathan2017machine,
  title={Machine learning: an applied econometric approach},
  author={Mullainathan, Sendhil and Spiess, Jann},
  journal={Journal of Economic Perspectives},
  volume={31},
  number={2},
  pages={87--106},
  year={2017}
}

@article{Chang2020,
  title={Double/debiased machine learning for difference-in-differences models},
  author={Chang, Neng-Chieh},
  journal={The Econometrics Journal},
  volume={23},
  number={2},
  pages={177--191},
  year={2020},
  publisher={Oxford University Press}
}

@article{athey2016recursive,
  title={Recursive partitioning for heterogeneous causal effects},
  author={Athey, Susan and Imbens, Guido},
  journal={Proceedings of the National Academy of Sciences},
  volume={113},
  number={27},
  pages={7353--7360},
  year={2016},
  publisher={National Acad Sciences}
}

@article{Allingham1972,
  title={Income tax evasion: A theoretical analysis},
  author={Allingham, Michael G and Sandmo, Agnar},
  journal={Journal of public economics},
  volume={1},
  number={3-4},
  pages={323--338},
  year={1972},
  publisher={North-Holland}
}

@article{Sandmo2005,
  title={The theory of tax evasion: A retrospective view},
  author={Sandmo, Agnar},
  journal={National tax journal},
  pages={643--663},
  year={2005},
  publisher={JSTOR}
}

@article{Holland1986,
  title={Statistics and causal inference},
  author={Holland, Paul W},
  journal={Journal of the American statistical Association},
  volume={81},
  number={396},
  pages={945--960},
  year={1986},
  publisher={Taylor \& Francis}
}

@article{bloom1984estimating,
  title={Estimating the effect of job-training programs, using longitudinal data: Ashenfelter's findings reconsidered},
  author={Bloom, Howard S},
  journal={Journal of Human Resources},
  pages={544--556},
  year={1984},
  publisher={JSTOR}
}
@article{robinson1988root,
  title={Root-N-consistent semiparametric regression},
  author={Robinson, Peter M},
  journal={Econometrica: Journal of the Econometric Society},
  pages={931--954},
  year={1988},
  publisher={JSTOR}
}
@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}
}