Fetching the paper…
Reading the bibliography…
Assessing the effects of a policy based on observational data from a different policy is a common problem across several high-stake decision-making domains, and several off-policy evaluation (OPE) techniques have been proposed.
Correlation and causation
Sewall Wright · 1921
Earlier work this paper cites.
A markovian decision process
Richard Bellman · 1957
Earlier work this paper cites.
Randomization and social policy evaluation
James J Heckman · 1992
Earlier work this paper cites.
Identification of causal effects using instrumental variables
Joshua D Angrist, Guido W Imbens, and Donald B Rubin · 1996
Earlier work this paper cites.
Policy-relevant treatment effects
James J Heckman and Edward Vytlacil · 2001
Earlier work this paper cites.
Dynamic multidrug therapies for hiv: Optimal and sti control approaches
Brian M Adams, Harvey T Banks, Hee-Dae Kwon, and Hien T Tran · 2004
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy
Benjamin Ribba, Gentian Kaloshi, Mathieu Peyre, Damien Ricard, Vincent Calvez, Michel Tod, Branka Čajavec-Bernard, Ahmed Idbaih, Dimitri Psimaras, Linda Dainese, et al · 2012
Earlier work this paper cites.
Single world intervention graphs (swigs): A unification of the counterfactual and graphical approaches to causality
Thomas S Richardson and James M Robins · 2013
Earlier work this paper cites.
Concurrent reinforcement learning from customer interactions
David Silver, Leonard Newnham, David Barker, Suzanne Weller, and Jason McFall · 2013
Earlier work this paper cites.
Offline policy evaluation across representations with applications to educational games
Travis Mandel, Yun-En Liu, Sergey Levine, Emma Brunskill, and Zoran Popovic · 2014
Earlier work this paper cites.
Doubly robust off-policy value evaluation for reinforcement learning
Nan Jiang and Lihong Li · 2016
Earlier work this paper cites.
Data-efficient off-policy policy evaluation for reinforcement learning
Philip Thomas and Emma Brunskill · 2016
Earlier work this paper cites.
The role of stage at diagnosis in colorectal cancer black–white survival disparities: a counterfactual causal inference approach
Linda Valeri, Jarvis T Chen, Xabier Garcia-Albeniz, Nancy Krieger, Tyler J VanderWeele, and Brent A Coull · 2016
Cited alongside, same era.
Combining kernel and model based learning for hiv therapy selection
Sonali Parbhoo, Jasmina Bogojeska, Maurizio Zazzi, Volker Roth, and Finale Doshi-Velez · 2017
Cited alongside, same era.
Double/debiased machine learning for treatment and structural parameters, 2018
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins · 2018
Cited alongside, same era.
Identifying causal effects with proxy variables of an unmeasured confounder
Wang Miao, Zhi Geng, and Eric J Tchetgen Tchetgen · 2018
Cited alongside, same era.
Improving counterfactual reasoning with kernelised dynamic mixing models
Sonali Parbhoo, Omer Gottesman, Andrew Slavin Ross, Matthieu Komorowski, Aldo Faisal, Isabella Bon, Volker Roth, and Finale Doshi-Velez · 2018
Cited alongside, same era.
A crash course in good and bad controls
Carlos Cinelli, Andrew Forney, and Judea Pearl · 2020
Later among the works it cites.
Coindice: Off-policy confidence interval estimation
Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, and Dale Schuurmans · 2020
Later among the works it cites.
Interpretable off-policy evaluation in reinforcement learning by highlighting influential transitions
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Celi, Emma Brunskill, and Finale Doshi-Velez · 2020
Later among the works it cites.
Causal inference: what if, 2020
Miguel A Hernán and James M Robins · 2020
Later among the works it cites.
Confounding-robust policy evaluation in infinite-horizon reinforcement learning
Nathan Kallus and Angela Zhou · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Reinforcement Learning, second edition: An Introduction
R.S. Sutton and A.G. Barto · 2018
Cited alongside, same era.
Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
Cited alongside, same era.
Combining parametric and nonparametric models for off-policy evaluation
Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, and Finale Doshi-Velez · 2019
Cited alongside, same era.
Off-policy estimation of long-term average outcomes with applications to mobile health
Peng Liao, Predrag Klasnja, and Susan Murphy · 2019
Cited alongside, same era.
Counterfactual off-policy evaluation with gumbel-max structural causal models
Michael Oberst and David Sontag · 2019
Cited alongside, same era.
Defining admissible rewards for high confidence policy evaluation
Niranjani Prasad, Barbara E Engelhardt, and Finale Doshi-Velez · 2019
Cited alongside, same era.
Jonathan G Richens, Ciarán M Lee, and Saurabh Johri · 2019
Cited alongside, same era.
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Later among the works it cites.
Off-policy policy evaluation for sequential decisions under unobserved confounding
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, and Emma Brunskill · 2020
Later among the works it cites.
Shaping control variates for off-policy evaluation
Sonali Parbhoo, Omer Gottesman, and Finale Doshi-Velez · 2020
Later among the works it cites.
Confounding feature acquisition for causal effect estimation
Shirly Wang, Seung Eun Yi, Shalmali Joshi, and Marzyeh Ghassemi · 2020
Later among the works it cites.
Learning deep features in instrumental variable regression
Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, and Arthur Gretton · 2020
Later among the works it cites.
Interpretable learning-to-defer for sequential decision-making
Shalmali Joshi*, Sonali Parbhoo*, and Finale Doshi-Velez · 2021
Later among the works it cites.
Estimating identifiable causal effects through double machine learning
Yonghan Jung, Jin Tian, and Elias Bareinboim · 2021
Later among the works it cites.
Learning under adversarial and interventional shifts
Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, and Himabindu Lakkaraju · 2021
Later among the works it cites.