% Data-driven feature separation

@inproceedings{kuang2017treatment,
  title={Treatment effect estimation with data-driven variable decomposition},
  author={Kuang, Kun and Cui, Peng and Li, Bo and Jiang, Meng and Yang, Shiqiang and Wang, Fei},
  booktitle={AAAI},
  volume={31},
  number={1},
  year={2017}
}

@inproceedings{hassanpour2020learning,
  title={Learning disentangled representations for counterfactual regression},
  author={Hassanpour, Negar and Greiner, Russell},
  booktitle={ICLR},
  year={2020}
}


@inproceedings{wang2023treatment,
  title={Treatment effect estimation with adjustment feature selection},
  author={Wang, Haotian and Kuang, Kun and Chi, Haoang and Yang, Longqi and Geng, Mingyang and Huang, Wanrong and Yang, Wenjing},
  booktitle={KDD},
  pages={2290--2301},
  year={2023}
}

@article{kuang2020data,
  title={Data-driven variable decomposition for treatment effect estimation},
  author={Kuang, Kun and Cui, Peng and Zou, Hao and Li, Bo and Tao, Jianrong and Wu, Fei and Yang, Shiqiang},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={34},
  number={5},
  pages={2120--2134},
  year={2020}
}

@article{wu2022learning,
  title={Learning decomposed representations for treatment effect estimation},
  author={Wu, Anpeng and Yuan, Junkun and Kuang, Kun and Li, Bo and Wu, Runze and Zhu, Qiang and Zhuang, Yueting and Wu, Fei},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={35},
  number={5},
  pages={4989--5001},
  year={2022}
}

@article{liu2024edvae,
  title={{EDVAE}: Disentangled latent factors models in counterfactual reasoning for individual treatment effects estimation},
  author={Liu, Yu and Wang, Jian and Li, Bing},
  journal={Information Sciences},
  volume={652},
  pages={119578},
  year={2024},
  publisher={Elsevier}
}

@article{chu2022disentangled,
  title={Disentangled representation for sequential treatment effect estimation},
  author={Chu, Jiebin and Zhang, Yaoyun and Huang, Fei and Si, Luo and Huang, Songfang and Huang, Zhengxing},
  journal={Computer Methods and Programs in Biomedicine},
  volume={226},
  pages={107175},
  year={2022}
}

@inproceedings{cheng2022learning,
  title={Learning disentangled representations for counterfactual regression via mutual information minimization},
  author={Cheng, Mingyuan and Liao, Xinru and Liu, Quan and Ma, Bin and Xu, Jian and Zheng, Bo},
  booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={1802--1806},
  year={2022}
}

% MMD

@article{gretton2012kernel,
  title={A kernel two-sample test},
  author={Gretton, Arthur and Borgwardt, Karsten M. and Rasch, Malte J and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
  journal={JMLR},
  volume={13},
  number={1},
  pages={723--773},
  year={2012}
}

% definitions 

@article{rubin1974estimating,
  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}
}

@article{abadie2002simple,
  title={Simple and bias-corrected matching estimators for average treatment effects},
  author={Abadie, Alberto and Imbens, Guido W.},
  journal={NBER Working Paper},
  number={t0283},
  year={2002}
}

% assumptions
@article{rosenbaum1983central,
  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}
}

@article{elwert2014endogenous,
  title={Endogenous selection bias: {T}he problem of conditioning on a collider variable},
  author={Elwert, Felix and Winship, Christopher},
  journal={Annual Review of Sociology},
  volume={40},
  pages={31--53},
  year={2014},
  publisher={Annual Reviews}
}

% nadaraya watson

@article{nadaraya1964estimating,
  title={On estimating regression},
  author={Nadaraya, Elizbar A.},
  journal={Theory of Probability \& Its Applications},
  volume={9},
  number={1},
  pages={141--142},
  year={1964}
}

@article{watson1964smooth,
  title={Smooth regression analysis},
  author={Watson, Geoffrey S.},
  journal={Sankhy{\=a}: Indian Journal of Statistics, Series A},
  pages={359--372},
  year={1964}
}

@article{ke2019expected,
  title={Expected conditional characteristic function-based measures for testing independence},
  author={Ke, Chenlu and Yin, Xiangrong},
  journal={Journal of American Statistical Association},
  year={2019}
}

@book{silverman1986density,
  title={Density Estimation for Statistics and Data Analysis},
  author={Silverman, Bernard W.},
  volume={26},
  year={1986},
  publisher={CRC Press}
}

% propensity-based CATE estimation

@article{abrevaya2015estimating,
  title={Estimating conditional average treatment effects},
  author={Abrevaya, Jason and Hsu, Yu-Chin and Lieli, Robert P.},
  journal={Journal of Business \& Economic Statistics},
  volume={33},
  number={4},
  pages={485--505},
  year={2015}
}

@article{zhou2021ipw,
  title={{On IPW-based estimation of conditional average treatment effects}},
  author={Zhou, Niwen and Zhu, Lixing},
  journal={Journal of Statistical Planning and Inference},
  volume={215},
  pages={1--22},
  year={2021}
}

% inferring effect modifiers

@article{tukey1949comparing,
  title={Comparing individual means in the analysis of variance},
  author={Tukey, John W.},
  journal={Biometrics},
  pages={99--114},
  year={1949}
}

@article{imai2013estimating,
  title={Estimating treatment effect heterogeneity in randomized program evaluation},
  author={Imai, Kosuke and Ratkovic, Marc},
  journal={Annals of Applied Statistics},
  volume={7},
  number={1},
  pages={443--470},
  year={2013}
}

@article{tian2014simple,
author = {Lu Tian and Ash A. Alizadeh and Andrew J. Gentles and Robert Tibshirani},
title = {A simple method for estimating interactions between a treatment and a large number of covariates},
journal = {Journal of American Statistical Association},
volume = {109},
number = {508},
pages = {1517-1532},
year  = {2014}
}

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

@article{zhao2022selective,
 author = {Zhao, Qingyuan and Small, Dylan S. and Ertefaie, Ashkan},
  title = {Selective inference for effect modification via the lasso},
  journal = {Journal of Royal Statistical Society: Series B (Statistical Methodology)},
  volume = {84},
  number = {2},
  pages = {382-413},
  year={2022}
}

@article{sechidis2021using,
  title={Using knockoffs for controlled predictive biomarker identification},
  author={Sechidis, Konstantinos and Kormaksson, Matthias and Ohlssen, David},
  journal={Statistics in Medicine},
  volume={40},
  number={25},
  pages={5453--5473},
  year={2021}
}


@article{gilad2021heterogeneous,
  title={Heterogeneous treatment effects in social networks}, 
  author={Amir Gilad and Harsh Parikh and Sudeepa Roy and Babak Salimi},
  year={2021},
  journal={arXiv preprint arXiv:2105.10591}
}

@inproceedings{hermansson2021discovering,
  title={{On discovering treatment-effect modifiers using virtual twins and causal forest ML in the presence of prognostic biomarkers}},
  author={Hermansson, Erik and Svensson, David},
  booktitle={International Conference on Computational Science and Its Applications},
  pages={624--640},
  year={2021}
}

@article{robinson1988root,
  title={Root-{N}-consistent semiparametric regression},
  author={Robinson, Peter M.},
  journal={Econometrica: Journal of Econometric Society},
  pages={931--954},
  year={1988}
}


% applications of effect modification discovery

@article{xie2020heterogeneous,
  title={Heterogeneous treatment effects on Children's cognitive/non-cognitive skills: A reevaluation of an influential early childhood intervention},
  author={Xie, Yu and Near, Christopher and Xu, Hongwei and Song, Xi},
  journal={Social Science Research},
  volume={86},
  pages={102389},
  year={2020}
}

@article{shalit2020can,
  title={Can we learn individual-level treatment policies from clinical data?},
  author={Shalit, Uri},
  journal={Biostatistics},
  volume={21},
  number={2},
  pages={359--362},
  year={2020}
}

@article{gao2021assessment,
  title={Assessment of heterogeneous treatment effect estimation accuracy via matching},
  author={Gao, Zijun and Hastie, Trevor and Tibshirani, Robert},
  journal={Statistics in Medicine},
  volume={40},
  number={17},
  pages={3990--4013},
  year={2021}
}

@inproceedings{sun2015causal,
  title={Causal inference via sparse additive models with application to online advertising},
  author={Sun, Wei and Wang, Pengyuan and Yin, Dawei and Yang, Jian and Chang, Yi},
  booktitle={AAAI},
  volume={29},
  number={1},
  year={2015}
}

@article{bottou2013counterfactual,
  title={Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising.},
  author={Bottou, L{\'e}on and Peters, Jonas and Qui{\~n}onero-Candela, Joaquin and Charles, Denis X and Chickering, D. Max and Portugaly, Elon and Ray, Dipankar and Simard, Patrice and Snelson, Ed},
  journal={JMLR},
  volume={14},
  number={11},
  year={2013}
}

@inproceedings{wang2015robust,
  title={Robust tree-based causal inference for complex ad effectiveness analysis},
  author={Wang, Pengyuan and Sun, Wei and Yin, Dawei and Yang, Jian and Chang, Yi},
  booktitle={WSDM},
  pages={67--76},
  year={2015}
}

@article{zeng2022uncovering,
  title={Uncovering interpretable potential confounders in electronic medical records},
  author={Zeng, Jiaming and Gensheimer, Michael F. and Rubin, Daniel L. and Athey, Susan and Shachter, Ross D.},
  journal={Nature Communications},
  volume={13},
  number={1},
  pages={1014},
  year={2022}
}

@article{curth2024using,
  title={Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities},
  author={Curth, Alicia and Peck, Richard W. and McKinney, Eoin and Weatherall, James and van Der Schaar, Mihaela},
  journal={Clinical Pharmacology \& Therapeutics},
  year={2024}
}

% Recent approaches

%% Nonparameteric Bayesian models
@inproceedings{alaa2017bayesian,
  title={{Bayesian inference of individualized treatment effects using multi-task Gaussian processes}},
  author={Alaa, Ahmed M. and van der Schaar, Mihaela},
  booktitle={NeurIPS},
  pages={3427--3435},
  year={2017}
}

@article{alaa2018bayesian,
  title={Bayesian nonparametric causal inference: Information rates and learning algorithms},
  author={Alaa, Ahmed M. and van der Schaar, Mihaela},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  volume={12},
  number={5},
  pages={1031--1046},
  year={2018}
}

@article{hill2011bayesian,
  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}
}


%% subspace matching
@inproceedings{chang2017informative,
  title={Informative subspace learning for counterfactual inference},
  author={Chang, Yale and Dy, Jennifer},
  booktitle={AAAI},
  pages={1770--1776},
  year={2017}
}

%% causal forest
@article{wager2018estimation,
  title={Estimation and inference of heterogeneous treatment effects using random forests},
  author={Wager, Stefan and Athey, Susan},
  journal={Journal of American Statistical Association},
  volume={113},
  number={523},
  pages={1228--1242},
  year={2018}
}

@article{hahn2020bayesian,
  title={Bayesian regression tree models for causal inference: {R}egularization, confounding, and heterogeneous effects},
  author={Hahn, P. Richard and Murray, Jared S. and Carvalho, Carlos M.},
  journal={Bayesian Analysis},
  volume={15},
  number={3},
  pages={965--1056},
  year={2020}
}

%% neural representation learning
@inproceedings{johansson2016learning,
  title={Learning representations for counterfactual inference},
  author={Johansson, Fredrik and Shalit, Uri and Sontag, David},
  booktitle={ICML},
  pages={3020--3029},
  year={2016}
}

@inproceedings{shalit2017estimating,
  title={Estimating individual treatment effect: {G}eneralization bounds and algorithms},
  author={Shalit, Uri and Johansson, Fredrik D. and Sontag, David},
  booktitle={ICML},
  pages={3076--3085},
  year={2017}
}

@inproceedings{yoon2018ganite,
  title={{GANITE: {E}stimation of individualized treatment effects using generative adversarial nets}},
  author={Yoon, Jinsung and Jordon, James and Van Der Schaar, Mihaela},
  booktitle={ICLR},
  year={2018}
}

@inproceedings{hassanpour2019counterfactual,
  title={CounterFactual Regression with Importance Sampling Weights.},
  author={Hassanpour, Negar and Greiner, Russell},
  booktitle={IJCAI},
  pages={5880--5887},
  year={2019}
}

@inproceedings{lim2018forecasting,
  title={Forecasting treatment responses over time using recurrent marginal structural networks},
  author={Lim, Bryan and Alaa, Ahmed and Schaar, Mihaela van der},
  booktitle={NeurIPS},
  pages={7494--7504},
  year={2018}
}

@inproceedings{zou2020counterfactual,
  title={Counterfactual prediction for bundle treatment},
  author={Zou, Hao and Cui, Peng and Li, Bo and Shen, Zheyan and Ma, Jianxin and Yang, Hongxia and He, Yue},
  booktitle={NeurIPS},
  pages={19705--19715},
  year={2020}
}

@inproceedings{wang2022generalization,
  title={Generalization bounds for estimating causal effects of continuous treatments},
  author={Wang, Xin and Lyu, Shengfei and Wu, Xingyu and Wu, Tianhao and Chen, Huanhuan},
  booktitle={NeurIPS},
  pages={8605--8617},
  year={2022}
}

% R-learner

@article{nie2021quasi,
  title={Quasi-oracle estimation of heterogeneous treatment effects},
  author={Nie, Xinkun and Wager, Stefan},
  journal={Biometrika},
  volume={108},
  number={2},
  pages={299--319},
  year={2021}
}

% S-learner, T-learner
@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 National Academy of Sciences},
  volume={116},
  number={10},
  pages={4156--4165},
  year={2019}
}


%% fairness-aware ML with bounds regularization

@inproceedings{chikahara2021learning,
  title={Learning individually fair classifier with path-specific causal-effect constraint},
  author={Chikahara, Yoichi and Sakaue, Shinsaku and Fujino, Akinori and Kashima, Hisashi},
  booktitle={AISTATS},
  pages={145--153},
  year={2021}
}








%% for supplemental materials

@article{muller1997integral,
  title={Integral probability metrics and their generating classes of functions},
  author={M{\"u}ller, Alfred},
  journal={Advances in Applied Probability},
  volume={29},
  number={2},
  pages={429--443},
  year={1997}
}

@techreport{hoeffding1961strong,
  title={The strong law of large numbers for {U}-statistics.},
  author={Hoeffding, Wassily},
  year={1961}
}

@book{serfling2009approximation,
  title={Approximation theorems of mathematical statistics},
  author={Serfling, Robert J.},
  volume={162},
  year={2009}
}

@article{wied2012consistency,
  title={Consistency of the kernel density estimator: {A} survey},
  author={Wied, Dominik and Wei{\ss}bach, Rafael},
  journal={Statistical Papers},
  volume={53},
  number={1},
  pages={1--21},
  year={2012}
}


%%%%% added (weighting-based, high-dimensional)

@inproceedings{greenewald2021high,
  title={High-dimensional feature selection for sample efficient treatment effect estimation},
  author={Greenewald, Kristjan and Shanmugam, Karthikeyan and Katz, Dmitriy},
  booktitle={AISTATS},
  pages={2224--2232},
  year={2021}
}

@inproceedings{assaad2021counterfactual,
  title={Counterfactual representation learning with balancing weights},
  author={Assaad, Serge and Zeng, Shuxi and Tao, Chenyang and Datta, Shounak and Mehta, Nikhil and Henao, Ricardo and Li, Fan and Carin, Lawrence},
  booktitle={AISTATS},
  pages={1972--1980},
  year={2021}
}



@inproceedings{li2017matching,
  title={Matching on balanced nonlinear representations for treatment effects estimation},
  author={Li, Sheng and Fu, Yun},
  booktitle={NeurIPS},
  pages={930--940},  
  year={2017}
}


@inproceedings{keith2020text,
  title={Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates},
  author={Keith, Katherine and Jensen, David and O’Connor, Brendan},
  booktitle={ACL},
  pages={5332--5344},
  year={2020}
}

@article{brookhart2006variable,
  title={Variable selection for propensity score models},
  author={Brookhart, M. Alan and Schneeweiss, Sebastian and Rothman, Kenneth J. and Glynn, Robert J. and Avorn, Jerry and St{\"u}rmer, Til},
  journal={American Journal of Epidemiology},
  volume={163},
  number={12},
  pages={1149--1156},
  year={2006}
}

@article{sauer2013review,
  title={A review of covariate selection for non-experimental comparative effectiveness research},
  author={Sauer, Brian C. and Brookhart, M. Alan and Roy, Jason and VanderWeele, Tyler},
  journal={Pharmacoepidemiology and Drug Safety},
  volume={22},
  number={11},
  pages={1139--1145},
  year={2013}
}

@article{zhu2020pareto,
  title={A {P}areto-smoothing method for causal inference using generalized Pareto distribution},
  author={Zhu, Fujin and Lu, Jie and Lin, Adi and Zhang, Guangquan},
  journal={Neurocomputing},
  volume={378},
  pages={142--152},
  year={2020}
}

@inproceedings{kaddour2021causal,
  title={Causal effect inference for structured treatments},
  author={Kaddour, Jean and Zhu, Yuchen and Liu, Qi and Kusner, Matt J. and Silva, Ricardo},
  booktitle={NeurIPS},
  pages={24841--24854},
  year={2021}
}

@inproceedings{cuturi2019differentiable,
  title={Differentiable ranking and sorting using optimal transport},
  author={Cuturi, Marco and Teboul, Olivier and Vert, Jean-Philippe},
  booktitle={NeurIPS},
  pages={6861--6871},
  year={2019}
}

@inproceedings{blondel2020fast,
  title={Fast differentiable sorting and ranking},
  author={Blondel, Mathieu and Teboul, Olivier and Berthet, Quentin and Djolonga, Josip},
  booktitle={ICML},
  pages={950--959},
  year={2020}
}

@article{vehtari2024pareto,
  title={Pareto smoothed importance sampling},
  author={Vehtari, Aki and Simpson, Daniel and Gelman, Andrew and Yao, Yuling and Gabry, Jonah},
  journal={JMLR},
  year={2024}
}

@article{hosking1987parameter,
  title={Parameter and quantile estimation for the generalized Pareto distribution},
  author={Hosking, Jonathan R.M. and Wallis, James R.},
  journal={Technometrics},
  volume={29},
  number={3},
  pages={339--349},
  year={1987}
}

@inproceedings{veitch2020adapting,
  title={Adapting text embeddings for causal inference},
  author={Veitch, Victor and Sridhar, Dhanya and Blei, David},
  booktitle={UAI},
  pages={919--928},
  year={2020}
}

@article{crump2009dealing,
  title={Dealing with limited overlap in estimation of average treatment effects},
  author={Crump, Richard K. and Hotz, V. Joseph and Imbens, Guido W. and Mitnik, Oscar A.},
  journal={Biometrika},
  volume={96},
  number={1},
  pages={187--199},
  year={2009}
}

@article{kang2018dataset,
  title={A dataset of peer reviews ({PeerRead}): Collection, insights and {NLP} applications},
  author={Kang, Dongyeop and Ammar, Waleed and Dalvi, Bhavana and Van Zuylen, Madeleine and Kohlmeier, Sebastian and Hovy, Eduard and Schwartz, Roy},
  journal={arXiv preprint arXiv:1804.09635},
  year={2018}
}

@article{ionides2008truncated,
  title={Truncated importance sampling},
  author={Ionides, Edward L.},
  journal={Journal of Computational and Graphical Statistics},
  volume={17},
  number={2},
  pages={295--311},
  year={2008}
}


%% Experiments

@article{macdorman1998infant,
  title={Infant mortality statistics from the linked birth/infant death data set--1995 period data},
  author={MacDorman, Marian F. and Atkinson, Jonnae O.},
  year={1998}
}

@article{shimoni2018benchmarking,
  title={Benchmarking framework for performance-evaluation of causal inference analysis},
  author={Shimoni, Yishai and Yanover, Chen and Karavani, Ehud and Goldschmnidt, Yaara},
  journal={arXiv preprint arXiv:1802.05046},
  year={2018}
}

@article{athey2019generalized,
  title={Generalized random forests},
  author={Athey, Susan and Tibshirani, Julie and Wager, Stefan},
  journal={Annals of Statistics},
  volume={47},
  number={2},
  pages={1148--1178},
  year={2019}
}

@article{chernozhukov2018double,
  title={Double/debiased machine learning for treatment and structural parameters: Double/debiased machine learning},
  author={Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Duflo, Esther and Hansen, Christian and Newey, Whitney and Robins, James},
  journal={Econometrics Journal},
  volume={21},
  number={1},
  year={2018}
}

@inproceedings{kingma2015,
  author    = {Kingma, Diederik and Ba, Jimmy},
  booktitle = {ICLR},
  title     = {Adam: A Method for Stochastic Optimization},
  year      = {2015}
}

@inproceedings{clevert2016accurate,
  author = {Clevert, Djork-Arné and Unterthiner, Thomas and Hochreiter, Sepp},
  booktitle = {ICLR},
  title = {Fast and Accurate Deep Network Learning by Exponential Linear Units ({ELU}s).},
  year ={2016}
}


@article{battocchi2019econml,
  title={Econ{ML}: {A} {P}ython package for {ML}-Based heterogeneous treatment effects estimation},
  author={Battocchi, Keith and Dillon, Eleanor and Hei, Maggie and Lewis, Greg and Oka, Paul and Oprescu, Miruna and Syrgkanis, Vasilis},
  journal={Version 0. 14.1},
  year={2019}
}

@article{pedregosa2011scikit,
  title={Scikit-learn: {M}achine learning in {P}ython},
  author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
  journal={JMLR},
  volume={12},
  number={Oct},
  pages={2825--2830},
  year={2011}
}

@inproceedings{paszke2019pytorch,
  title={Py{T}orch: An imperative style, high-performance deep learning library},
  author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others},
  booktitle={NeurIPS},
  volume={32},
  year={2019}
}

@inproceedings{curth2021nonparametric,
  title={Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms},
  author={Curth, Alicia and van der Schaar, Mihaela},
    year={2021},
      pages={1810--1818},
  booktitle={AISTATS}
}

@article{arviz_2019,
  year = {2019},
  volume = {4},
  number = {33},
  pages = {1143},
  author = {Ravin Kumar and Colin Carroll and Ari Hartikainen and Osvaldo Martin},
  title = {Arvi{Z} a unified library for exploratory analysis of {B}ayesian models in {P}ython},
  journal = {Journal of Open Source Software}
}

%% Related work

@inproceedings{chikahara22a,
  title = {Feature selection for discovering distributional treatment effect modifiers},
  author = {Chikahara, Yoichi and Yamada, Makoto and Kashima, Hisashi},
  booktitle = {UAI},
  pages = {400--410},
  year = {2022},
  volume = {180}
}


@inproceedings{horii2024uncertainty,
  title={Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model},
  author={Horii, Shunsuke and Chikahara, Yoichi},
  booktitle={AAAI},
  volume={38},
  number={18},
  pages={20420--20429},
  year={2024}
}

