@incollection{Bengio+chapter2007,
author = {Bengio, Yoshua and LeCun, Yann},
booktitle = {Large Scale Kernel Machines},
publisher = {MIT Press},
title = {Scaling Learning Algorithms Towards {AI}},
year = {2007}
}

@article{Hinton06,
author = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye},
journal = {Neural Computation},
pages = {1527--1554},
title = {A Fast Learning Algorithm for Deep Belief Nets},
volume = {18},
year = {2006}
}

@book{goodfellow2016deep,
title={Deep learning},
author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
volume={1},
year={2016},
publisher={MIT Press}
}

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@article{hill,
  title={Bayesian nonparametric modeling for causal inference},
  author={Hill, Jennifer L},
  journal={Journal of Computational and Graphical Statistics},
  volume={20},
  number={1},
  pages={217--240},
  year={2011},
  publisher={Taylor \& Francis}
}

@article{glass2013causal,
  title={Causal inference in public health},
  author={Glass, Thomas A and Goodman, Steven N and Hernán, Miguel A and Samet, Jonathan M},
  journal={Annual Review of Public Health},
  volume={34},
  pages={61--75},
  year={2013},
  publisher={Annual Reviews},
  doi={10.1146/annurev-publhealth-031811-124606},
  pmid={23297653},
  pmcid={PMC4079266}
}

@article{feuerriegel2024causal,
  title={Causal machine learning for predicting treatment outcomes},
  author={Feuerriegel, Stefan and Frauen, Dennis and Melnychuk, Valentyn and Schweisthal, Jonas and Hess, Konstantin and Curth, Alicia and Bauer, Stefan and Kilbertus, Niki and Kohane, Isaac S and van der Schaar, Mihaela},
  journal={Nature Medicine},
  volume={30},
  number={4},
  pages={958--968},
  year={2024},
  publisher={Nature Publishing Group US New York}
}


@article{athey,
  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{billingsley1995probability,
  title={Probability and measure. 3rd wiley},
  author={Billingsley, P},
  journal={New York},
  year={1995}
}
@inproceedings{
fang2024causalstonet,
title={Causal-StoNet: Causal Inference for High-Dimensional Complex Data},
author={Yaxin Fang and Faming Liang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=BtZ7vCt5QY}
}

@article{shi2019adapting,
  title={Adapting neural networks for the estimation of treatment effects},
  author={Shi, Claudia and Blei, David and Veitch, Victor},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{
melnychuk2024bounds,
title={Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation},
author={Valentyn Melnychuk and Dennis Frauen and Stefan Feuerriegel},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=d3xKPQVjSc}
}

@inproceedings{
richens2024robust,
title={Robust agents learn causal world models},
author={Jonathan Richens and Tom Everitt},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=pOoKI3ouv1}
}

@book{durrett2019probability,
  title={Probability: theory and examples},
  author={Durrett, Rick},
  volume={49},
  year={2019},
  publisher={Cambridge university press}
}

@article{greenblatt2011priority,
  title={Priority issues concerning HIV infection among women},
  author={Greenblatt, Ruth M},
  journal={Women's Health Issues},
  volume={21},
  number={6},
  pages={S266--S271},
  year={2011},
  publisher={Elsevier}
}

@article{gandhi2005eligibility,
  title={Eligibility criteria for HIV clinical trials and generalizability of results: the gap between published reports and study protocols},
  author={Gandhi, Monica and Ameli, Niloufar and Bacchetti, Peter and Sharp, Gerald B and French, Audrey L and Young, Mary and Gange, Stephen J and Anastos, Kathryn and Holman, Susan and Levine, Alexandra and others},
  journal={Aids},
  volume={19},
  number={16},
  pages={1885--1896},
  year={2005},
  publisher={LWW}
}

@inproceedings{hatt2022generalizing,
  title={Generalizing off-policy learning under sample selection bias},
  author={Hatt, Tobias and Tschernutter, Daniel and Feuerriegel, Stefan},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={769--779},
  year={2022},
  organization={PMLR}
}

@article{hammer1996,
  title={A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter},
  author={Hammer, Scott M and Katzenstein, David A and Hughes, Michael D and Gundacker, Holly and Schooley, Robert T and Haubrich, Richard H and Henry, W Keith and Lederman, Michael M and Phair, John P and Niu, Manette and others},
  journal={New England Journal of Medicine},
  volume={335},
  number={15},
  pages={1081--1090},
  year={1996},
  publisher={Mass Medical Soc}
}

@inproceedings{shalit,
  title={Estimating individual treatment effect: generalization bounds and algorithms},
  author={Shalit, Uri and Johansson, Fredrik D and Sontag, David},
  booktitle={International Conference on Machine Learning},
  pages={3076--3085},
  year={2017},
  organization={PMLR}
}


@InProceedings{schweisthal24a,
  title = 	 {Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments},
  author =       {Schweisthal, Jonas and Frauen, Dennis and Van Der Schaar, Mihaela and Feuerriegel, Stefan},
  booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
  pages = 	 {43967--43985},
  year = 	 {2024},
  volume = 	 {235},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {21--27 Jul},
  publisher =    {PMLR},
}

@inproceedings{guo2023estimating,
  title={Estimating heterogeneous treatment effects: Mutual information bounds and learning algorithms},
  author={Guo, Xingzhuo and Zhang, Yuchen and Wang, Jianmin and Long, Mingsheng},
  booktitle={International Conference on Machine Learning},
  pages={12108--12121},
  year={2023},
  organization={PMLR}
}

@article{chor2024three,
  title={Three-Year Outcomes for Low-Income Parents of Young Children in a Two-Generation Education Program},
  author={Chor, Elise and Chase-Lansdale, P. Lindsay and Sommer, Teresa Eckrich and Sabol, Terri and Tighe, Lauren and Brooks-Gunn, Jeanne and Yoshikawa, Hirokazu and Morris, Amanda and King, Christopher},
  journal={Journal of Research on Educational Effectiveness},
  volume={0},
  number={0},
  pages={1--42},
  year={2024},
  publisher={Taylor \& Francis}
}

@inproceedings{kallus2019interval,
  title={Interval estimation of individual-level causal effects under unobserved confounding},
  author={Kallus, Nathan and Mao, Xiaojie and Zhou, Angela},
  booktitle={The 22nd international conference on artificial intelligence and statistics},
  pages={2281--2290},
  year={2019},
  organization={PMLR}
}

@inproceedings{hatt2024sequential,
  title={Sequential deconfounding for causal inference with unobserved confounders},
  author={Hatt, Tobias and Feuerriegel, Stefan},
  booktitle={Causal Learning and Reasoning},
  pages={934--956},
  year={2024},
  organization={PMLR}
}

@book{hernan2020causal,
  title={Causal Inference: What If},
  author={Hern{\'a}n, Miguel A and Robins, James M},
  year={2020},
  publisher={Chapman \& Hall/CRC},
  address={Boca Raton}
}


@article{hill2011,
  title={Bayesian nonparametric modeling for causal inference},
  author={Hill, Jennifer L},
  journal={Journal of Computational and Graphical Statistics},
  volume={20},
  number={1},
  pages={217--240},
  year={2011},
  publisher={Taylor \& Francis}
}

@article{smith2005does,
  title={Does matching overcome LaLonde's critique of nonexperimental estimators?},
  author={Smith, Jeffrey A and Todd, Petra E},
  journal={Journal of econometrics},
  volume={125},
  number={1-2},
  pages={305--353},
  year={2005},
  publisher={Elsevier}
}

@article{kallus2018confounding,
  title={Confounding-robust policy improvement},
  author={Kallus, Nathan and Zhou, Angela},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@article{lalonde1986evaluating,
  title={Evaluating the econometric evaluations of training programs with experimental data},
  author={LaLonde, Robert J},
  journal={The American economic review},
  pages={604--620},
  year={1986},
  publisher={JSTOR}
}
@book{rosenbaum2002,
  title={Observational Studies},
  author={Rosenbaum, Paul R.},
  edition={2nd},
  year={2002},
  publisher={Springer},
  address={New York}
}


@article{kallus2018removing,
  title={Removing hidden confounding by experimental grounding},
  author={Kallus, Nathan and Puli, Aahlad Manas and Shalit, Uri},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@article{li2024removing,
  title={Removing hidden confounding in recommendation: a unified multi-task learning approach},
  author={Li, Haoxuan and Wu, Kunhan and Zheng, Chunyuan and Xiao, Yanghao and Wang, Hao and Geng, Zhi and Feng, Fuli and He, Xiangnan and Wu, Peng},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}
@article{alaa,
  title={Bayesian inference of individualized treatment effects using multi-task gaussian processes},
  author={Alaa, Ahmed M and Van Der Schaar, Mihaela},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
@article{hatt2022combining,
  title={Combining observational and randomized data for estimating heterogeneous treatment effects},
  author={Hatt, Tobias and Berrevoets, Jeroen and Curth, Alicia and Feuerriegel, Stefan and van der Schaar, Mihaela},
  journal={arXiv preprint arXiv:2202.12891},
  year={2022}
}

@inproceedings{fernandes2017transfer,
  title={Transfer learning with partial observability applied to cervical cancer screening},
  author={Fernandes, Kelwin and Cardoso, Jaime S and Fernandes, Jessica},
  booktitle={Pattern Recognition and Image Analysis: 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings 8},
  pages={243--250},
  year={2017},
  organization={Springer}
}

@book{coles2001introduction,
  title={An introduction to statistical modeling of extreme values},
  author={Coles, Stuart and Bawa, Joanna and Trenner, Lesley and Dorazio, Pat},
  volume={208},
  year={2001},
  publisher={Springer}
}

@article{ramey1992infant,
  title={Infant Health and Development Program for low birth weight, premature infants: Program elements, family participation, and child intelligence},
  author={Ramey, Craig T and Bryant, Donna M and Wasik, Barbara H and Sparling, Joseph J and Fendt, Kaye H and La Vange, Lisa M},
  journal={Pediatrics},
  volume={89},
  number={3},
  pages={454--465},
  year={1992},
  publisher={American Academy of Pediatrics}
}

@article{davison2012spatial,
author = {A. C. Davison and S. A. Padoan and M. Ribatet},
title = {{Statistical Modeling of Spatial Extremes}},
volume = {27},
journal = {Statistical Science},
number = {2},
publisher = {Institute of Mathematical Statistics},
pages = {161 -- 186},
keywords = {Annual maximum analysis, Bayesian hierarchical model, Brown–Resnick process, Composite likelihood, copula, environmental data analysis, Gaussian process, generalized extreme-value distribution, Geostatistics, latent variable, Max-stable process, statistics of extremes},
year = {2012},
doi = {10.1214/11-STS376},
URL = {https://doi.org/10.1214/11-STS376}
}

@inproceedings{johanson,
  title={Learning representations for counterfactual inference},
  author={Johansson, Fredrik and Shalit, Uri and Sontag, David},
  booktitle={International conference on machine learning},
  pages={3020--3029},
  year={2016},
  organization={PMLR}
}

@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{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}
}

@book{pearl,
author = {Pearl, Judea},
title = {Causality: Models, Reasoning and Inference},
year = {2009},
isbn = {052189560X},
publisher = {Cambridge University Press},
address = {USA},
edition = {2nd},
}


@book{imbens2015causal,
  title={Causal inference in statistics, social, and biomedical sciences},
  author={Imbens, Guido W and Rubin, Donald B},
  year={2015},
  publisher={Cambridge University Press}
}

@article{imbens2024causal,
  title={Causal inference in the social sciences},
  author={Imbens, Guido W},
  journal={Annual Review of Statistics and Its Application},
  volume={11},
  year={2024},
  publisher={Annual Reviews}
}

@article{neyman1923,
  title={Sur les applications de la th{\'e}orie des probabilit{\'e}s aux experiences agricoles: Essai des principes},
  author={Neyman, Jersey},
  journal={Roczniki Nauk Rolniczych},
  volume={10},
  number={1},
  pages={1--51},
  year={1923}
}

@article{kunzel2019metalearners,
  title={Metalearners for estimating heterogeneous treatment effects using machine learning},
  author={K{\"u}nzel, S{\"o}ren R and Sekhon, Jasjeet S and Bickel, Peter J and Yu, Bin},
  journal={Proceedings of the national academy of sciences},
  volume={116},
  number={10},
  pages={4156--4165},
  year={2019},
  publisher={National Acad Sciences}
}

@article{imbens2004,
  title={Nonparametric estimation of average treatment effects under exogeneity: A review},
  author={Imbens, Guido W},
  journal={Review of Economics and statistics},
  volume={86},
  number={1},
  pages={4--29},
  year={2004},
  publisher={MIT Press 238 Main St., Suite 500, Cambridge, MA 02142-1046, USA journals~…}
}

@article{rubin2005,
  title={Causal inference using potential outcomes},
  author={Rubin, Donal},
  journal={Journal of the American Statistical Association},
  volume={100},
  number={469},
  pages={322--31},
  year={2005}
}

@article{keele2015statistics,
  title={The statistics of causal inference: A view from political methodology},
  author={Keele, Luke},
  journal={Political Analysis},
  volume={23},
  number={3},
  pages={313--335},
  year={2015},
  publisher={Cambridge University Press}
}

@article{abadie2002bootstrap,
  title={Bootstrap tests for distributional treatment effects in instrumental variable models},
  author={Abadie, Alberto},
  journal={Journal of the American statistical Association},
  volume={97},
  number={457},
  pages={284--292},
  year={2002},
  publisher={Taylor \& Francis}
}

@article{chernozhukov2005iv,
  title={An IV model of quantile treatment effects},
  author={Chernozhukov, Victor and Hansen, Christian},
  journal={Econometrica},
  volume={73},
  number={1},
  pages={245--261},
  year={2005},
  publisher={Wiley Online Library}
}

@article{chernozhukov2013inference,
  title={Inference on counterfactual distributions},
  author={Chernozhukov, Victor and Fern{\'a}ndez-Val, Iv{\'a}n and Melly, Blaise},
  journal={Econometrica},
  volume={81},
  number={6},
  pages={2205--2268},
  year={2013},
  publisher={Wiley Online Library}
}
@article{diaz2017efficient,
  title={Efficient estimation of quantiles in missing data models},
  author={D{\'\i}az, Iv{\'a}n},
  journal={Journal of Statistical Planning and Inference},
  volume={190},
  pages={39--51},
  year={2017},
  publisher={Elsevier}
}

@article{firpo2007efficient,
  title={Efficient semiparametric estimation of quantile treatment effects},
  author={Firpo, Sergio},
  journal={Econometrica},
  volume={75},
  number={1},
  pages={259--276},
  year={2007},
  publisher={Wiley Online Library}
}
@article{kennedy2021semiparametric,
  title={Semiparametric counterfactual density estimation},
  author={Kennedy, Edward H and Balakrishnan, Sivaraman and Wasserman, Larry},
  journal={arXiv preprint arXiv:2102.12034},
  year={2021}
}
@article{frolich2013unconditional,
  title={Unconditional quantile treatment effects under endogeneity},
  author={Fr{\"o}lich, Markus and Melly, Blaise},
  journal={Journal of Business \& Economic Statistics},
  volume={31},
  number={3},
  pages={346--357},
  year={2013},
  publisher={Taylor \& Francis}
}

@article{zhang2012causal,
  title={Causal inference on quantiles with an obstetric application},
  author={Zhang, Zhiwei and Chen, Zhen and Troendle, James F and Zhang, Jun},
  journal={Biometrics},
  volume={68},
  number={3},
  pages={697--706},
  year={2012},
  publisher={Wiley Online Library}
}
@article{giessing2021debiased,
  title={Debiased Inference on Heterogeneous Quantile Treatment Effects with Regression Rank-Scores},
  author={Giessing, Alexander and Wang, Jingshen},
  journal={arXiv preprint arXiv:2102.01753},
  year={2021}
}
@article{kennedy2020towards,
  title={Towards optimal doubly robust estimation of heterogeneous causal effects},
  author={Kennedy, Edward H},
  journal={arXiv preprint arXiv:2004.14497},
  year={2020}
}

@article{deuber2021estimation,
  title={Estimation and inference of extremal quantile treatment effects for heavy-tailed distributions},
  author={Deuber, David and Li, Jinzhou and Engelke, Sebastian and Maathuis, Marloes H},
  journal={arXiv preprint arXiv:2110.06627},
  year={2021}
}

@article{rosenbaum1983,
  title={The central role of the propensity score in observational studies for causal effects},
  author={Rosenbaum, Paul R and Rubin, Donald B},
  journal={Biometrika},
  volume={70},
  number={1},
  pages={41--55},
  year={1983},
  publisher={Oxford University Press}
}

@article{doubly,
  title={Doubly robust estimation of causal effects},
  author={Funk, Michele Jonsson and Westreich, Daniel and Wiesen, Chris and St{\"u}rmer, Til and Brookhart, M Alan and Davidian, Marie},
  journal={American journal of epidemiology},
  volume={173},
  number={7},
  pages={761--767},
  year={2011},
  publisher={Oxford University Press}
}

@article{hirano2003,
  title={Efficient estimation of average treatment effects using the estimated propensity score},
  author={Hirano, Keisuke and Imbens, Guido W and Ridder, Geert},
  journal={Econometrica},
  volume={71},
  number={4},
  pages={1161--1189},
  year={2003},
  publisher={Wiley Online Library}
}


@book{embrechts2013modelling,
  title={Modelling extremal events: for insurance and finance},
  author={Embrechts, Paul and Kl{\"u}ppelberg, Claudia and Mikosch, Thomas},
  volume={33},
  year={2013},
  publisher={Springer Science \& Business Media}
}

@book{mcneil2015quantitative,
  title={Quantitative risk management: concepts, techniques and tools-revised edition},
  author={McNeil, Alexander J and Frey, R{\"u}diger and Embrechts, Paul},
  year={2015},
  publisher={Princeton university press}
}

@book{morgan2015counterfactuals,
  title={Counterfactuals and causal inference},
  author={Morgan, Stephen L and Winship, Christopher},
  year={2015},
  publisher={Cambridge University Press}
}
@book{roncalli2020handbook,
  title={Handbook of financial risk management},
  author={Roncalli, Thierry},
  year={2020},
  publisher={CRC press}
}

@article{veitch2020sense,
  title={Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding},
  author={Veitch, Victor and Zaveri, Anisha},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={10999--11009},
  year={2020}
}
@inproceedings{oprescu2023b,
  title={B-learner: Quasi-oracle bounds on heterogeneous causal effects under hidden confounding},
  author={Oprescu, Miruna and Dorn, Jacob and Ghoummaid, Marah and Jesson, Andrew and Kallus, Nathan and Shalit, Uri},
  booktitle={International Conference on Machine Learning},
  pages={26599--26618},
  year={2023},
  organization={PMLR}
}

@book{popper2005logic,
  title={The logic of scientific discovery},
  author={Popper, Karl},
  year={2005},
  publisher={Routledge}
}

@article{colnet2024causal,
  title={Causal inference methods for combining randomized trials and observational studies: a review},
  author={Colnet, B{\'e}n{\'e}dicte and Mayer, Imke and Chen, Guanhua and Dieng, Awa and Li, Ruohong and Varoquaux, Ga{\"e}l and Vert, Jean-Philippe and Josse, Julie and Yang, Shu},
  journal={Statistical science},
  volume={39},
  number={1},
  pages={165--191},
  year={2024},
  publisher={Institute of Mathematical Statistics}
}

