Fetching the paper…
Reading the bibliography…
Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder.
The Tariff on Animal and Vegetable Oils
P. Wright · 1928
Earlier work this paper cites.
Generalized inverses in reproducing kernel spaces: An approach to regularization of linear operator equations
M. Z. Nashed and G. Wahba · 1974
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
P. R. Rosenbaum and D. B. Rubin · 1983
Earlier work this paper cites.
Linear Integral Equations
R. Kress · 1999
Earlier work this paper cites.
Nonparametric estimation of average treatment effects under exogeneity: A review
G. W. Imbens · 2003
Earlier work this paper cites.
Bayesian nonparametric modeling for causal inference
J. Hill · 2011
Earlier work this paper cites.
Measuring the price responsiveness of gasoline demand: Economic shape restrictions and nonparametric demand estimation
R. Blundell, J. Horowitz, and M. Parey · 2012
Earlier work this paper cites.
Foundations of Machine Learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2012
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2014
Earlier work this paper cites.
Measurement bias and effect restoration in causal inference
M. Kuroki and J. Pearl · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
How the timing of grade retention affects outcomes: Identification and estimation of time-varying treatment effects
J. Fruehwirth, S. Navarro, and Y. Takahashi · 2016
Cited alongside, same era.
Learning representations for counterfactual inference
F. Johansson, U. Shalit, and D. Sontag · 2016
Cited alongside, same era.
Deep IV: A flexible approach for counterfactual prediction
J. Hartford, G. Lewis, K. Leyton-Brown, and M. Taddy · 2017
Cited alongside, same era.
Causal effect inference with deep latent-variable models
C. Louizos, U. Shalit, J. M. Mooij, D. Sontag, R. S. Zemel, and M. Welling · 2017
Cited alongside, same era.
dSprites: Disentanglement testing sprites dataset, 2017
L. Matthey, I. Higgins, D. Hassabis, and A. Lerchner · 2017
Cited alongside, same era.
Proxy controls and panel data, 2018
B. Deaner · 2018
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Later among the works it cites.
Kernel instrumental variable regression
R. Singh, M. Sahani, and A. Gretton · 2019
Later among the works it cites.
Provably efficient neural estimation of structural equation models: An adversarial approach
L. Liao, Y.-L. Chen, Z. Yang, B. Dai, M. Kolar, and Z. Wang · 2020
Later among the works it cites.
Optimizing millions of hyperparameters by implicit differentiation
J. Lorraine, P. Vicol, and D. Duvenaud · 2020
Later among the works it cites.
Identifying effects of multiple treatments in the presence of unmeasured confounding, 2020
W. Miao, W. Hu, E. L. Ogburn, and X. 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…
Estimation of individual treatment effect in latent confounder models via adversarial learning
C. Lee, N. Mastronarde, and M. van der Schaar · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
Cited alongside, same era.
Representation learning for treatment effect estimation from observational data
L. Yao, S. Li, Y. Li, M. Huai, J. Gao, and A. Zhang · 2018
Cited alongside, same era.
Deep generalized method of moments for instrumental variable analysis
A. Bennett, N. Kallus, and T. Schnabel · 2019
Cited alongside, same era.
The role of over-parametrization in generalization of neural networks
B. Neyshabur, Z. Li, S. Bhojanapalli, Y. LeCun, and N. Srebro · 2019
Cited alongside, same era.
Identifying causal effects with proxy variables of an unmeasured confounder
W. Miao, Z. Geng, and E. Tchetgen Tchetgen
Cited in the paper.
Kernel methods for unobserved confounding: Negative controls, proxies, and instruments, 2020
R. Singh · 2020
Later among the works it cites.
An introduction to proximal causal learning, 2020
E. J. T. Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao · 2020
Later among the works it cites.
Off-policy evaluation in partially observable environments
G. Tennenholtz, U. Shalit, and S. Mannor · 2020
Later among the works it cites.
Proximal causal learning with kernels: Two-stage estimation and moment restriction
A. Mastouri, Y. Zhu, L. Gultchin, A. Korba, R. Silva, M. Kusner, A. Gretton, and K. Muandet · 2021
Closest in time.
A proxy variable view of shared confounding
Y. Wang and D. Blei · 2021
Closest in time.
Learning deep features in instrumental variable regression
L. Xu, Y. Chen, S. Srinivasan, N. de Freitas, A. Doucet, and A. Gretton · 2021
Closest in time.