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2012.10315
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Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments
18 December 2020
Rahul Singh
CML
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Papers citing
"Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments"
31 / 31 papers shown
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Automating the Selection of Proxy Variables of Unmeasured Confounders
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Ruichu Cai
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243
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0
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Regularized DeepIV with Model Selection
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Hui Lan
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261
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0
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Doubly Robust Proximal Causal Learning for Continuous Treatments
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Yong Wu
Yanwei Fu
Shouyan Wang
Xinwei Sun
397
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22 Sep 2023
A Convex Framework for Confounding Robust Inference
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Takafumi Kanamori
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326
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21 Sep 2023
Kernel Single Proxy Control for Deterministic Confounding
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Liyuan Xu
Arthur Gretton
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339
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0
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Contagion Effect Estimation Using Proximal Embeddings
CLEaR (CLEaR), 2023
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Elena Zheleva
293
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0
04 Jun 2023
A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations
Siyu Chen
Yitan Wang
Zhaoran Wang
Zhuoran Yang
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243
3
0
20 Mar 2023
Kernel Conditional Moment Constraints for Confounding Robust Inference
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Kei Ishikawa
Niao He
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263
3
0
26 Feb 2023
Proximal Causal Learning of Conditional Average Treatment Effects
International Conference on Machine Learning (ICML), 2023
Erik Sverdrup
Yifan Cui
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381
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0
26 Jan 2023
Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency
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Peng Ding
Martin J. Wainwright
Peter L. Bartlett
OffRL
272
12
0
16 Jan 2023
Optimal Treatment Regimes for Proximal Causal Learning
Neural Information Processing Systems (NeurIPS), 2022
Tao Shen
Yifan Cui
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409
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0
19 Dec 2022
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models
Neural Information Processing Systems (NeurIPS), 2022
Rui Miao
Zhengling Qi
Xiaoke Zhang
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340
14
0
21 Sep 2022
Inference on Strongly Identified Functionals of Weakly Identified Functions
Annual Conference Computational Learning Theory (COLT), 2022
Andrew Bennett
Nathan Kallus
Xiaojie Mao
Whitney Newey
Vasilis Syrgkanis
Masatoshi Uehara
430
23
0
17 Aug 2022
Proximal Survival Analysis to Handle Dependent Right Censoring
Andrew Ying
288
13
0
15 Aug 2022
Instrumented Common Confounding
Christian Tien
CML
312
2
0
26 Jun 2022
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
International Conference on Learning Representations (ICLR), 2022
Miao Lu
Yifei Min
Zhaoran Wang
Zhuoran Yang
OffRL
428
26
0
26 May 2022
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
Neural Information Processing Systems (NeurIPS), 2022
Benjamin Kompa
David R. Bellamy
Thomas Kolokotrones
J. M. Robins
Andrew L. Beam
354
16
0
19 May 2022
Controlling for Latent Confounding with Triple Proxies
Ben Deaner
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151
2
0
28 Apr 2022
Proximal Causal Inference for Marginal Counterfactual Survival Curves
Andrew Ying
Yifan Cui
E. T. Tchetgen
218
12
0
27 Apr 2022
Long-term Causal Inference Under Persistent Confounding via Data Combination
Guido Imbens
Nathan Kallus
Xiaojie Mao
Yuhao Wang
CML
587
59
0
15 Feb 2022
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
International Conference on Machine Learning (ICML), 2021
C. Shi
Masatoshi Uehara
Jiawei Huang
Nan Jiang
OffRL
402
31
0
12 Nov 2021
Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection
Rahul Singh
266
1
0
09 Nov 2021
Many Proxy Controls
Ben Deaner
AI4CE
194
7
0
08 Oct 2021
The Proximal ID Algorithm
I. Shpitser
Zach Wood-Doughty
E. T. Tchetgen
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386
18
0
15 Aug 2021
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
Neural Information Processing Systems (NeurIPS), 2021
Liyuan Xu
Heishiro Kanagawa
Arthur Gretton
CML
403
46
0
07 Jun 2021
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
International Conference on Machine Learning (ICML), 2021
Afsaneh Mastouri
Yuchen Zhu
Limor Gultchin
Anna Korba
Ricardo M. A. Silva
Matt J. Kusner
Arthur Gretton
Krikamol Muandet
CML
634
75
0
10 May 2021
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
AmirEmad Ghassami
Andrew Ying
I. Shpitser
E. T. Tchetgen
325
48
0
07 Apr 2021
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
Nathan Kallus
Xiaojie Mao
Masatoshi Uehara
CML
441
75
0
25 Mar 2021
Semiparametric proximal causal inference
Journal of the American Statistical Association (JASA), 2020
Yifan Cui
Hongming Pu
Xu Shi
Wang Miao
E. T. Tchetgen Tchetgen
540
135
0
17 Nov 2020
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