Reinforcement Learning with Continuous Actions Under Unmeasured Confounding | Paper Citation Record

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Reinforcement Learning with Continuous Actions Under Unmeasured Confounding , " * write output.state after.block = add.period write newline

Reference 1

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Reinforcement Learning with Continuous Actions Under Unmeasured Confounding write newline

Reference 2

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Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 3

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Observation 6fb5d4e1-7c06-417a-ad6f-51111b68b2b2 · outbound

This paper cites (2008), Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path, Machine Learning, 71, 89--129.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2008), Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path, Machine Learning, 71, 89--129

Reference 4

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This paper cites (2017), Breaking the curse of dimensionality with convex neural networks, The Journal of Machine Learning Research, 18, 629--681.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2017), Breaking the curse of dimensionality with convex neural networks, The Journal of Machine Learning Research, 18, 629--681

Reference 5

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This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 6

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This paper cites and Kallus, N.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Kallus, N

Reference 7

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This paper cites (2021), Off-policy evaluation in infinite-horizon reinforcement learning with latent confounders, in International Conference on Artificial Intelligence and Statistics, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2021), Off-policy evaluation in infinite-horizon reinforcement learning with latent confounders, in International Conference on Artificial Intelligence and Statistics, PMLR, pp

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.745039Z

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Observation b13a54ca-1684-4dbe-9b79-434d67a8905e · outbound

This paper cites and Kennedy, E.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Kennedy, E

Reference 9

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This paper cites u derl, J., Schmiedeberg, C., Castiglioni, L., Arr \'a nz Becker, O., Buhr, P., Fu , D., Ludwig, V., Schr \.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding u derl, J., Schmiedeberg, C., Castiglioni, L., Arr \'a nz Becker, O., Buhr, P., Fu , D., Ludwig, V., Schr \

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.717702Z

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This paper cites W., Yuan, Z., Zhou, S., Panerati, J., and Schoellig, A.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding W., Yuan, Z., Zhou, S., Panerati, J., and Schoellig, A

Reference 11

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This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 12

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This paper cites Jump Interval-Learning for Individualized Decision Making.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Jump Interval-Learning for Individualized Decision Making

Reference 13

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This paper cites (2022), Reinforcement learning from partial observation: Linear function approximation with provable sample efficiency, in International Conference on Machine Learning, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2022), Reinforcement learning from partial observation: Linear function approximation with provable sample efficiency, in International Conference on Machine Learning, PMLR, pp

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.670739Z

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Observation d2bf2f97-5c20-4453-ad01-9ca77c8cee3c · outbound

This paper cites and Qi, Z.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Qi, Z

Reference 15

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This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 16

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Observation 4e3b8511-e91b-4714-bcc7-1e34e89d429f · outbound

This paper cites (2023), Semiparametric proximal causal inference, Journal of the American Statistical Association, 1--12.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2023), Semiparametric proximal causal inference, Journal of the American Statistical Association, 1--12

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.623843Z

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This paper cites and Tchetgen Tchetgen, E.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Tchetgen Tchetgen, E

Reference 18

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Observation 29ef32ba-ce66-481e-a0ba-3e3a1705fd6d · outbound

This paper cites (2020), Minimax estimation of conditional moment models, Advances in Neural Information Processing Systems, 33, 12248--12262.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2020), Minimax estimation of conditional moment models, Advances in Neural Information Processing Systems, 33, 12248--12262

Reference 19

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Observation 9867b87c-580b-44ac-83f4-0b8b48f2b823 · outbound

This paper cites (2011), On the completeness condition in nonparametric instrumental problems, Econometric Theory, 27, 460--471.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2011), On the completeness condition in nonparametric instrumental problems, Econometric Theory, 27, 460--471

Reference 20

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This paper cites (2016), Regularized policy iteration with nonparametric function spaces, Journal of Machine Learning Research, 17, 1--66.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2016), Regularized policy iteration with nonparametric function spaces, Journal of Machine Learning Research, 17, 1--66

Reference 21

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Observation d2ed938a-2311-4043-9762-36a090b59f61 · outbound

This paper cites H., Moreau, Y., Murphy, S.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding H., Moreau, Y., Murphy, S

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.551560Z

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Observation a4e09640-c797-485e-8822-c14d95c61cff · outbound

This paper cites Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes

Reference 23

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This paper cites and Gu, S.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Gu, S

Reference 24

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This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 25

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This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 26

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Observation 7a388575-444c-4319-a6c5-0497d81ae671 · outbound

This paper cites (2022), Provably efficient offline reinforcement learning for partially observable markov decision processes, in International Conference on Machine Learning, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2022), Provably efficient offline reinforcement learning for partially observable markov decision processes, in International Conference on Machine Learning, PMLR, pp

Reference 27

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This paper cites Soft Actor-Critic Algorithms and Applications.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Soft Actor-Critic Algorithms and Applications

Reference 28

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Observation a0c36dbc-7f04-4b38-b8ec-feabeff990fe · outbound

This paper cites (2021), Bootstrapping fitted q-evaluation for off-policy inference, in International Conference on Machine Learning, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2021), Bootstrapping fitted q-evaluation for off-policy inference, in International Conference on Machine Learning, PMLR, pp

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This paper cites W., Lazaric, A., Ghavamzadeh, M., and Munos, R.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding W., Lazaric, A., Ghavamzadeh, M., and Munos, R

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.461072Z

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Observation 2c93aa95-6b44-413d-b947-93e017b98bb9 · outbound

This paper cites A Policy Gradient Method for Confounded POMDPs.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding A Policy Gradient Method for Confounded POMDPs

Reference 31

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This paper cites (2020), Sample-efficient reinforcement learning of undercomplete pomdps, Advances in Neural Information Processing Systems, 33, 18530--18539.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2020), Sample-efficient reinforcement learning of undercomplete pomdps, Advances in Neural Information Processing Systems, 33, 18530--18539

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.445717Z

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Observation 679dbfa6-731c-40af-aaa2-0efcd270d2e8 · outbound

This paper cites (2022), Doubly robust distributionally robust off-policy evaluation and learning, in International Conference on Machine Learning, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2022), Doubly robust distributionally robust off-policy evaluation and learning, in International Conference on Machine Learning, PMLR, pp

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Observation 41912fe6-7a55-40dc-bba5-46b1dde2d57c · outbound

This paper cites and Uehara, M.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Uehara, M

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.416571Z

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This paper cites and Zhou, A.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Zhou, A

Reference 35

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This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 36

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Observation 86f2fbbe-2e73-422d-82bb-c9ee1b033d39 · outbound

This paper cites B., Volkmann, J.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding B., Volkmann, J

Reference 37

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Observation 841944cf-839a-4b90-9e6d-a176aa198162 · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Offline Reinforcement Learning with Implicit Q-Learning

Reference 38

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Observation 8864b3c9-f08c-4ef0-a21a-8980cb3ed206 · outbound

This paper cites (1989), Linear integral equations, vol.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (1989), Linear integral equations, vol

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Observation 03655345-0d94-4581-9c58-68eb06c787cf · outbound

This paper cites (2020), Conservative q-learning for offline reinforcement learning, Advances in Neural Information Processing Systems, 33, 1179--1191.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2020), Conservative q-learning for offline reinforcement learning, Advances in Neural Information Processing Systems, 33, 1179--1191

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.342241Z

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Observation 8849dbc8-95c4-47ae-9032-6fe596954bd0 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 41

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Observation f88e3568-60d5-4d53-8989-91dbb3c9016c · outbound

This paper cites (2019), Batch policy learning under constraints, in International Conference on Machine Learning, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2019), Batch policy learning under constraints, in International Conference on Machine Learning, PMLR, pp

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.312552Z

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Observation b7f6d36c-6ed5-467d-a10f-e67b1502c283 · outbound

This paper cites (2018), Deep reinforcement learning in continuous action spaces: a case study in the game of simulated curling, in International conference on machine learning, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2018), Deep reinforcement learning in continuous action spaces: a case study in the game of simulated curling, in International conference on machine learning, PMLR, pp

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.298204Z

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Observation 8048a3b7-f55b-4017-9e54-1f6617564eb2 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

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Observation 14bd91e6-4c14-4b02-9245-b2dc72627b3f · outbound

This paper cites Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:14.457368Z

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Observation 70bb7568-6d3e-4162-86a0-2cb47449696a · outbound

This paper cites (2023), Quasi-optimal Reinforcement Learning with Continuous Actions, in The Eleventh International Conference on Learning Representations.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2023), Quasi-optimal Reinforcement Learning with Continuous Actions, in The Eleventh International Conference on Learning Representations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.282669Z

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Observation d0436792-f256-4aa7-bdde-4aeb45b17928 · outbound

This paper cites (2021), Off-policy estimation of long-term average outcomes with applications to mobile health, Journal of the American Statistical Association, 116, 382--391.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2021), Off-policy estimation of long-term average outcomes with applications to mobile health, Journal of the American Statistical Association, 116, 382--391

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.265633Z

Source-reported events for the cited work

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Observation af9514e0-1a5c-4152-88ea-09e1ae96af7c · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:54:15.250381Z

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Observation 73b4f8ed-f353-4222-9a7e-006ea9ebf242 · outbound

This paper cites Continuous control with deep reinforcement learning.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Continuous control with deep reinforcement learning

Reference 49

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Observation 0e94eefd-784f-4d04-9bfa-9acd74d2424f · outbound

This paper cites (2018), Breaking the curse of horizon: Infinite-horizon off-policy estimation, Advances in neural information processing systems, 31.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2018), Breaking the curse of horizon: Infinite-horizon off-policy estimation, Advances in neural information processing systems, 31

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.234853Z

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Observation 2a5f2215-1b5a-4ce8-9779-ade6115e83c1 · outbound

This paper cites Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes

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Observation 0f8924a3-0a2d-4df5-92b6-271946ed8cb1 · outbound

This paper cites J., Laber, E.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding J., Laber, E

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.219656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2d7fbf26-4a78-4e5b-9105-730ec6aad78d · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:54:15.203969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 356b302c-a8b7-4be8-aa11-789b56f312a8 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:14.498384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93f237ba-a115-49e9-98e9-9a4756e8fc95 · outbound

This paper cites A., Veness, J., Bellemare, M.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding A., Veness, J., Bellemare, M

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.179126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c198ad41-3aaa-4cbb-9fb4-b77187b50c60 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:54:15.164405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ea625b0f-788b-4480-ac5c-fa78767a645b · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:54:15.149351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e4a26aa4-66a6-4696-8ef2-d0abb2353646 · outbound

This paper cites (2000), Eligibility traces for off-policy policy evaluation, Computer Science Department Faculty Publication Series, 80.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2000), Eligibility traces for off-policy policy evaluation, Computer Science Department Faculty Publication Series, 80

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.134256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb3bbccb-0449-4776-98fa-674978f528d4 · outbound

This paper cites (2023), Proximal learning for individualized treatment regimes under unmeasured confounding, Journal of the American Statistical Association, 1--14.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2023), Proximal learning for individualized treatment regimes under unmeasured confounding, Journal of the American Statistical Association, 1--14

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.118195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d3d81530-ba02-40af-b8b8-e5fa33af6952 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:54:15.102803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 97084137-1bcb-48e2-900e-7e0333532a06 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:54:15.087809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 07b18144-1ed5-4b4a-9859-895647d7f1fa · outbound

This paper cites (2022 c ), Off-policy confidence interval estimation with confounded markov decision process, Journal of the American Statistical Association, 1--12.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2022 c ), Off-policy confidence interval estimation with confounded markov decision process, Journal of the American Statistical Association, 1--12

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.071842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8eb2c479-d3e8-497c-8163-4f098ac330b4 · outbound

This paper cites an unresolved cited work.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:54:15.056559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a206f1f6-67d3-45de-b359-cffd17e5a38e · outbound

This paper cites An Introduction to Proximal Causal Learning.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding An Introduction to Proximal Causal Learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:14.542761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e6742b5-5241-4b4f-b984-ea5e1d6c44f3 · outbound

This paper cites and Brunskill, E.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Brunskill, E

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.037610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b22ce68a-5aad-4f34-a0d5-285f3cf0ffdd · outbound

This paper cites (2020), Minimax weight and q-function learning for off-policy evaluation, in International Conference on Machine Learning, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2020), Minimax weight and q-function learning for off-policy evaluation, in International Conference on Machine Learning, PMLR, pp

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.020578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b3f6020-ec07-4882-a829-8ed66983dec8 · outbound

This paper cites (2024), Future-dependent value-based off-policy evaluation in pomdps, Advances in Neural Information Processing Systems, 36.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2024), Future-dependent value-based off-policy evaluation in pomdps, Advances in Neural Information Processing Systems, 36

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:15.000899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed0aa84a-7f48-49c7-993a-2d307ef8202c · outbound

This paper cites and Groothuis-Oudshoorn, K.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Groothuis-Oudshoorn, K

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.985762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aff82c72-59d1-47d5-907b-b6d4222f706c · outbound

This paper cites and Zou, S.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Zou, S

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.971136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3e1da517-73bf-45f0-a826-4367b3fdb266 · outbound

This paper cites (2019), Towards optimal off-policy evaluation for reinforcement learning with marginalized importance sampling, Advances in neural information processing systems, 32.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2019), Towards optimal off-policy evaluation for reinforcement learning with marginalized importance sampling, Advances in neural information processing systems, 32

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.955283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0a1d9ce9-2d9b-4746-8d99-b9c128bb581c · outbound

This paper cites (2023), An instrumental variable approach to confounded off-policy evaluation, in International Conference on Machine Learning, PMLR, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2023), An instrumental variable approach to confounded off-policy evaluation, in International Conference on Machine Learning, PMLR, pp

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.939449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b62d42a6-c9b9-4314-a023-203d356b22a9 · outbound

This paper cites and Bareinboim, E.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding and Bareinboim, E

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.924013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 686d4d10-0ba5-4f96-a399-6911a249dcc7 · outbound

This paper cites (2020), Causal imitation learning with unobserved confounders, Advances in neural information processing systems, 33, 12263--12274.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2020), Causal imitation learning with unobserved confounders, Advances in neural information processing systems, 33, 12263--12274

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.907724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 98108e71-ba89-4819-8277-83b9d4093515 · outbound

This paper cites On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:54:14.671602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a9b94b24-e0ac-4371-8991-def5ab75dc3b · outbound

This paper cites (2024), Bi-Level Offline Policy Optimization with Limited Exploration, Advances in Neural Information Processing Systems, 36.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2024), Bi-Level Offline Policy Optimization with Limited Exploration, Advances in Neural Information Processing Systems, 36

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.891413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8d09bd4e-b1cd-40e3-9026-7d253601b194 · outbound

This paper cites (2024 a ), Policy learning for individualized treatment regimes on infinite time horizon, in Statistics in Precision Health: Theory, Methods and Applications, Springer, pp.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2024 a ), Policy learning for individualized treatment regimes on infinite time horizon, in Statistics in Precision Health: Theory, Methods and Applications, Springer, pp

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.874788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5c19e899-4c9a-4945-8ca5-103002d52080 · outbound

This paper cites Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:14.601000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fe62085-e851-42d9-827b-f2b8ace0bd17 · outbound

This paper cites (2024 b ), Estimating optimal infinite horizon dynamic treatment regimes via pt-learning, Journal of the American Statistical Association, 119, 625--638.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2024 b ), Estimating optimal infinite horizon dynamic treatment regimes via pt-learning, Journal of the American Statistical Association, 119, 625--638

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.857615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7798237d-f28a-4154-8fe1-f5271244b8da · outbound

This paper cites (2020), Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving, Transportation Research Part C: Emerging Technologies, 117, 102662.

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding (2020), Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving, Transportation Research Part C: Emerging Technologies, 117, 102662

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:14.841403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers