\begin{thebibliography}{40}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
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  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Abadie and Imbens(2006)]{abadie2006large}
Alberto Abadie and Guido~W Imbens.
\newblock Large sample properties of matching estimators for average treatment
  effects.
\newblock \emph{Econometrica}, 74\penalty0 (1):\penalty0 235--267, 2006.

\bibitem[B{\"u}hlmann and {\'C}evid(2020)]{buhlmann2020deconfounding}
Peter B{\"u}hlmann and Domagoj {\'C}evid.
\newblock Deconfounding and causal regularisation for stability and external
  validity.
\newblock \emph{International Statistical Review}, 88:\penalty0 S114--S134,
  2020.

\bibitem[Chen(2007)]{chen2007large}
Xiaohong Chen.
\newblock Large sample sieve estimation of semi-nonparametric models.
\newblock \emph{Handbook of Econometrics}, 6:\penalty0 5549--5632, 2007.

\bibitem[Chernozhukov et~al.(2017)Chernozhukov, Hansen, and
  Liao]{chernozhukov2017lava}
Victor Chernozhukov, Christian Hansen, and Yuan Liao.
\newblock A lava attack on the recovery of sums of dense and sparse signals.
\newblock \emph{The Annals of Statistics}, 45\penalty0 (1):\penalty0 39--76,
  2017.

\bibitem[Chu et~al.(2023)Chu, Lu, and Yang]{chu2022targeted}
Jianing Chu, Wenbin Lu, and Shu Yang.
\newblock Targeted optimal treatment regime learning using summary statistics.
\newblock \emph{Biometrika}, 2023.
\newblock \doi{10.1093/biomet/asad020}.

\bibitem[Colnet et~al.(2020)Colnet, Mayer, Chen, Dieng, Li, Varoquaux, Vert,
  Josse, and Yang]{https://doi.org/10.48550/arxiv.2011.08047}
Bénédicte Colnet, Imke Mayer, Guanhua Chen, Awa Dieng, Ruohong Li, Gaël
  Varoquaux, Jean-Philippe Vert, Julie Josse, and Shu Yang.
\newblock Causal inference methods for combining randomized trials and
  observational studies: a review, 2020.
\newblock URL \url{https://arxiv.org/abs/2011.08047}.

\bibitem[Fan and Li(2001)]{fan2001variable}
Jianqing Fan and Runze Li.
\newblock Variable selection via nonconcave penalized likelihood and its oracle
  properties.
\newblock \emph{Journal of the American Statistical Association}, 96\penalty0
  (456):\penalty0 1348--1360, 2001.

\bibitem[Fan and Peng(2004)]{fan2004nonconcave}
Jianqing Fan and Heng Peng.
\newblock Nonconcave penalized likelihood with a diverging number of
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\newblock \emph{The Annals of Statistics}, 32\penalty0 (3):\penalty0 928--961,
  2004.

\bibitem[Fan et~al.(2014)Fan, Xue, and Zou]{fan2014strong}
Jianqing Fan, Lingzhou Xue, and Hui Zou.
\newblock Strong oracle optimality of folded concave penalized estimation.
\newblock \emph{The Annals of Statistics}, 42\penalty0 (3):\penalty0 819 --
  849, 2014.

\bibitem[Gilotte et~al.(2018)Gilotte, Calauz{\`e}nes, Nedelec, Abraham, and
  Doll{\'e}]{gilotte2018offline}
Alexandre Gilotte, Cl{\'e}ment Calauz{\`e}nes, Thomas Nedelec, Alexandre
  Abraham, and Simon Doll{\'e}.
\newblock Offline a/b testing for recommender systems.
\newblock In \emph{Proceedings of the Eleventh ACM International Conference on
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\bibitem[Greenland et~al.(1999)Greenland, Pearl, and
  Robins]{greenland1999causal}
Sander Greenland, Judea Pearl, and James~M Robins.
\newblock Causal diagrams for epidemiologic research.
\newblock \emph{Epidemiology}, 10\penalty0 (1):\penalty0 37--48, 1999.

\bibitem[Hern{\'a}n and Robins(2016)]{hernan2016using}
Miguel~A Hern{\'a}n and James~M Robins.
\newblock Using big data to emulate a target trial when a randomized trial is
  not available.
\newblock \emph{American Journal of Epidemiology}, 183\penalty0 (8):\penalty0
  758--764, 2016.

\bibitem[Hobbs et~al.(2012)Hobbs, Sargent, and Carlin]{hobbs2012commensurate}
Brian~P Hobbs, Daniel~J Sargent, and Bradley~P Carlin.
\newblock Commensurate priors for incorporating historical information in
  clinical trials using general and generalized linear models.
\newblock \emph{Bayesian Analysis}, 7\penalty0 (3):\penalty0 639–674, 2012.

\bibitem[Hobbs et~al.(2013)Hobbs, Carlin, and Sargent]{hobbs2013adaptive}
Brian~P Hobbs, Bradley~P Carlin, and Daniel~J Sargent.
\newblock Adaptive adjustment of the randomization ratio using historical
  control data.
\newblock \emph{Clinical Trials}, 10\penalty0 (3):\penalty0 430--440, 2013.

\bibitem[Ibrahim and Chen(2000)]{ibrahim2000power}
Joseph~G Ibrahim and Ming-Hui Chen.
\newblock Power prior distributions for regression models.
\newblock \emph{Statistical Science}, 15\penalty0 (1):\penalty0 46--60, 2000.

\bibitem[Kim and Kwon(2012)]{kim2012global}
Yongdai Kim and Sunghoon Kwon.
\newblock Global optimality of nonconvex penalized estimators.
\newblock \emph{Biometrika}, 99\penalty0 (2):\penalty0 315--325, 2012.

\bibitem[Lee et~al.(2022)Lee, Yang, and
  Wang]{https://doi.org/10.48550/arxiv.2201.06595}
Dasom Lee, Shu Yang, and Xiaofei Wang.
\newblock Doubly robust estimators for generalizing treatment effects on
  survival outcomes from randomized controlled trials to a target population,
  2022.
\newblock URL \url{https://arxiv.org/abs/2201.06595}.

\bibitem[Lin et~al.(2019)Lin, Gamalo-Siebers, and Tiwari]{lin2019propensity}
Junjing Lin, Margaret Gamalo-Siebers, and Ram Tiwari.
\newblock Propensity-score-based priors for bayesian augmented control design.
\newblock \emph{Pharmaceutical Statistics}, 18\penalty0 (2):\penalty0 223--238,
  2019.

\bibitem[Neuenschwander et~al.(2009)Neuenschwander, Branson, and
  Spiegelhalter]{neuenschwander2009note}
Beat Neuenschwander, Michael Branson, and David~J Spiegelhalter.
\newblock A note on the power prior.
\newblock \emph{Statistics in Medicine}, 28\penalty0 (28):\penalty0 3562--3566,
  2009.

\bibitem[Pocock(1976)]{pocock1976combination}
Stuart~J Pocock.
\newblock The combination of randomized and historical controls in clinical
  trials.
\newblock \emph{Journal of Chronic Diseases}, 29\penalty0 (3):\penalty0
  175--188, 1976.

\bibitem[Rubin(1974)]{rubin1974estimating}
Donald~B Rubin.
\newblock Estimating causal effects of treatments in randomized and
  nonrandomized studies.
\newblock \emph{Journal of Educational Psychology}, 66\penalty0 (5):\penalty0
  688--701, 1974.

\bibitem[Rubin(1978)]{rubin1978bayesian}
Donald~B Rubin.
\newblock Bayesian inference for causal effects: The role of randomization.
\newblock \emph{The Annals of Statistics}, 6\penalty0 (1):\penalty0 34--58,
  1978.

\bibitem[Rubin(2007)]{rubin2007design}
Donald~B Rubin.
\newblock The design versus the analysis of observational studies for causal
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\newblock \emph{Statistics in Medicine}, 26\penalty0 (1):\penalty0 20--36,
  2007.

\bibitem[Rubin and Thomas(1996)]{rubin1996matching}
Donald~B Rubin and Neal Thomas.
\newblock Matching using estimated propensity scores: relating theory to
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\newblock \emph{Biometrics}, 52\penalty0 (1):\penalty0 249--264, 1996.

\bibitem[Schmidli et~al.(2014)Schmidli, Gsteiger, Roychoudhury, O'Hagan,
  Spiegelhalter, and Neuenschwander]{schmidli2014robust}
Heinz Schmidli, Sandro Gsteiger, Satrajit Roychoudhury, Anthony O'Hagan, David
  Spiegelhalter, and Beat Neuenschwander.
\newblock Robust meta-analytic-predictive priors in clinical trials with
  historical control information.
\newblock \emph{Biometrics}, 70\penalty0 (4):\penalty0 1023--1032, 2014.

\bibitem[Spiegelhalter(2004)]{spiegelhalter2004incorporating}
David~J Spiegelhalter.
\newblock Incorporating bayesian ideas into health-care evaluation.
\newblock \emph{Statistical Science}, 19\penalty0 (1):\penalty0 156--174, 2004.

\bibitem[Splawa-Neyman et~al.(1990)Splawa-Neyman, Dabrowska, and
  Speed]{neyman1923application}
Jerzy Splawa-Neyman, D.~M. Dabrowska, and T.~P. Speed.
\newblock On the application of probability theory to agricultural experiments.
  essay on principles. section 9.
\newblock \emph{Statistical Science}, 5\penalty0 (4):\penalty0 465--472, 1990.

\bibitem[Stuart and Rubin(2008)]{stuart2008matching}
Elizabeth~A Stuart and Donald~B Rubin.
\newblock Matching with multiple control groups with adjustment for group
  differences.
\newblock \emph{Journal of Educational and Behavioral Statistics}, 33\penalty0
  (3):\penalty0 279--306, 2008.

\bibitem[Tibshirani(1996)]{tibshirani1996regression}
Robert Tibshirani.
\newblock Regression shrinkage and selection via the lasso.
\newblock \emph{Journal of the Royal Statistical Society: Series B
  (Methodological)}, 58\penalty0 (1):\penalty0 267--288, 1996.

\bibitem[Viele et~al.(2014)Viele, Berry, Neuenschwander, Amzal, Chen, Enas,
  Hobbs, Ibrahim, Kinnersley, Lindborg, et~al.]{viele2014use}
Kert Viele, Scott Berry, Beat Neuenschwander, Billy Amzal, Fang Chen, Nathan
  Enas, Brian Hobbs, Joseph~G Ibrahim, Nelson Kinnersley, Stacy Lindborg,
  et~al.
\newblock Use of historical control data for assessing treatment effects in
  clinical trials.
\newblock \emph{Pharmaceutical Statistics}, 13\penalty0 (1):\penalty0 41--54,
  2014.

\bibitem[Wang et~al.(2023)Wang, Susukida, Mojtabai, Amin-Esmaeili, and
  Rosenblum]{wang2021model}
Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, and
  Michael Rosenblum.
\newblock Model-robust inference for clinical trials that improve precision by
  stratified randomization and covariate adjustment.
\newblock \emph{Journal of the American Statistical Association}, 118\penalty0
  (542):\penalty0 1152--1163, 2023.

\bibitem[Wang and Zhou(2019)]{wang2019double}
Wenjia Wang and Yi-Hui Zhou.
\newblock A double penalty model for interpretability, 2019.
\newblock URL \url{https://arxiv.org/abs/1909.06263}.

\bibitem[Wu and Yang(2022)]{wu2022integrative}
Lili Wu and Shu Yang.
\newblock Integrative $r$-learner of heterogeneous treatment effects combining
  experimental and observational studies.
\newblock In \emph{Proceedings of the First Conference on Causal Learning and
  Reasoning}, volume 177 of \emph{Proceedings of Machine Learning Research},
  pages 904--926, 2022.

\bibitem[Xing et~al.(2021)Xing, Yang, and Yaosheng]{xing2021adaptive}
Bing Xing, Ning Yang, and Xu~Yaosheng.
\newblock Adaptive estimation of multivariate regression with hidden variables.
\newblock \emph{The Annals of Statistics}, 50\penalty0 (2):\penalty0 640 --
  672, 2021.

\bibitem[Yang et~al.(2022)Yang, Zeng, and Wang]{yang2022improved}
Shu Yang, Donglin Zeng, and Xiaofei Wang.
\newblock Improved inference for heterogeneous treatment effects using
  real-world data subject to hidden confounding, 2022.
\newblock URL \url{https://arxiv.org/abs/2007.12922}.

\bibitem[Yang et~al.(2023)Yang, Gao, Zeng, and Wang]{yang2022elastic}
Shu Yang, Chenyin Gao, Donglin Zeng, and Xiaofei Wang.
\newblock Elastic integrative analysis of randomized trial and real-world data
  for treatment heterogeneity estimation.
\newblock \emph{Journal of the Royal Statistical Society Series B: Statistical
  Methodology}, 2023.
\newblock \doi{10.1093/jrsssb/qkad017}.

\bibitem[Yu et~al.(2021)Yu, Lu, Yang, and Ghosh]{yu2021multiplicative}
Miao Yu, Wenbin Lu, Shu Yang, and Pulak Ghosh.
\newblock Multiplicative structural nested mean model for zero-inflated
  outcomes.
\newblock \emph{Biometrika}, 103\penalty0 (1):\penalty0 1--17, 2021.

\bibitem[Yuan et~al.(2019)Yuan, Liu, Zhu, Lu, and Palm]{yuan2019design}
Jiacheng Yuan, Jeen Liu, Ray Zhu, Ying Lu, and Ulo Palm.
\newblock Design of randomized controlled confirmatory trials using historical
  control data to augment sample size for concurrent controls.
\newblock \emph{Journal of Biopharmaceutical Statistics}, 29\penalty0
  (3):\penalty0 558--573, 2019.

\bibitem[Zhang(2010)]{zhang2010nearly}
Cun-Hui Zhang.
\newblock Nearly unbiased variable selection under minimax concave penalty.
\newblock \emph{The Annals of Statistics}, 38\penalty0 (2):\penalty0 894--942,
  2010.

\bibitem[Zou(2006)]{zou2006adaptive}
Hui Zou.
\newblock The adaptive lasso and its oracle properties.
\newblock \emph{Journal of the American Statistical Association}, 101\penalty0
  (476):\penalty0 1418--1429, 2006.

\end{thebibliography}
