Fetching the paper…
Reading the bibliography…
One of the central elements of any causal inference is an object called structural causal model (SCM), which represents a collection of mechanisms and exogenous sources of random variation of the system under investigation (Pearl, 2000).
Statistical Theory of Extreme Values and Some Practical Applications: A Series of Lectures
Gumbel, E. (1954) · 1954
Earlier work this paper cites.
Probabilistic Reasoning in Intelligent Systems
Pearl, J. (1988) · 1988
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · 1991
Earlier work this paper cites.
Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
Leshno, M., Lin, V. Y., Pinkus, A., and Schocken, S. (1993) · 1993
Earlier work this paper cites.
The probability integral transform and related results
Angus, J. E. (1994) · 1994
Earlier work this paper cites.
Counterfactual Probabilities: Computational Methods, Bounds, and Applications
Balke, A. and Pearl, J. (1994) · 1994
Earlier work this paper cites.
Causal diagrams for empirical research
Pearl, J. (1995) · 1995
Earlier work this paper cites.
A clinical trial of the effects of dietary patterns on blood pressure
Appel, L. J., Moore, T. J., Obarzanek, E., Vollmer, W. M., Svetkey, L. P., Sacks, F. M., Bray, G. A., Vogt, T. M., Cutler, J. A., Windhauser, M. M., and et al. (1997) · 1997
Earlier work this paper cites.
Causality: Models, Reasoning, and Inference
Pearl, J. (2000) · 2000
Earlier work this paper cites.
Causation, Prediction, and Search
Spirtes, P., Glymour, C. N., and Scheines, R. (2000) · 2000
Earlier work this paper cites.
Statistical Inference
Casella, G. and Berger, R. (2001) · 2001
Earlier work this paper cites.
A General Identification Condition for Causal Effects
Tian, J. and Pearl, J. (2002) · 2002
Earlier work this paper cites.
A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., and Smola, A. (2007) · 2007
Earlier work this paper cites.
On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
Zhang, J. (2008) · 2008
Earlier work this paper cites.
A framework for contrastive self-supervised learning and designing a new approach
Falcon, W. and Cho, K. (2020) · 2009
Earlier work this paper cites.
Local Characterizations of Causal Bayesian Networks
Bareinboim, E., Brito, C., and Pearl, J. (2012) · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
Towards end-to-end speech recognition with recurrent neural networks
Graves, A. and Jaitly, N. (2014) · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
Earlier work this paper cites.
Bandits with unobserved confounders: A causal approach
Bareinboim, E., Forney, A., and Pearl, J. (2015) · 2015
Earlier work this paper cites.
Made: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H. (2015) · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S. (2015) · 2015
Cited alongside, same era.
Causal inference and the data-fusion problem
Bareinboim, E. and Pearl, J. (2016) · 2016
Cited alongside, same era.
XGBoost: A scalable tree boosting system
Chen, T. and Guestrin, C. (2016) · 2016
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
Cited alongside, same era.
Learning representations for counterfactual inference
Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G. (2018) · 2018
Later among the works it cites.
Representation learning for treatment effect estimation from observational data
Yao, L., Li, S., Li, Y., Huai, M., Gao, J., and Zhang, A. (2018) · 2018
Later among the works it cites.
GANITE: Estimation of individualized treatment effects using generative adversarial nets
Yoon, J., Jordon, J., and van der Schaar, M. (2018) · 2018
Later among the works it cites.
Causal identification under Markov equivalence: Completeness results
Jaber, A., Zhang, J., and Bareinboim, E. (2019) · 2019
Later among the works it cites.
Characterization and learning of causal graphs with latent variables from soft interventions
Kocaoglu, M., Jaber, A., Shanmugam, K., and Bareinboim, E. (2019) · 2019
Later among the works it cites.
General Identifiability with Arbitrary Surrogate Experiments
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Johansson, F. D., Shalit, U., and Sontag, D. (2016) · 2016
Cited alongside, same era.
A kernelized stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J., and Jordan, M. (2016) · 2016
Cited alongside, same era.
Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017) · 2017
Cited alongside, same era.
Counterfactual Data-Fusion for Online Reinforcement Learners
Forney, A., Pearl, J., and Bareinboim, E. (2017) · 2017
Cited alongside, same era.
Matching on balanced nonlinear representations for treatment effects estimation
Li, S. and Fu, Y. (2017) · 2017
Cited alongside, same era.
SGDR: stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F. (2017) · 2017
Cited alongside, same era.
Lee, S., Correa, J. D., and Bareinboim, E. (2019) · 2019
Later among the works it cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F. (2019) · 2019
Later among the works it cites.
Adapting neural networks for the estimation of treatment effects
Shi, C., Blei, D. M., and Veitch, V. (2019) · 2019
Later among the works it cites.
On Pearl’s Hierarchy and the Foundations of Causal Inference
Bareinboim, E., Correa, J. D., Ibeling, D., and Icard, T. (2020) · 2020
Later among the works it cites.
A meta-transfer objective for learning to disentangle causal mechanisms
Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., and Pal, C. (2020) · 2020
Later among the works it cites.
Differentiable causal discovery from interventional data
Brouillard, P., Lachapelle, S., Lacoste, A., Lacoste-Julien, S., and Drouin, A. (2020) · 2020
Later among the works it cites.
General transportability of soft interventions: Completeness results
Correa, J. and Bareinboim, E. (2020) · 2020
Later among the works it cites.
A survey of learning causality with data
Guo, R., Cheng, L., Li, J., Hahn, P. R., and Liu, H. (2020) · 2020
Later among the works it cites.
Causal discovery from soft interventions with unknown targets: Characterization and learning
Jaber, A., Kocaoglu, M., Shanmugam, K., and Bareinboim, E. (2020) · 2020
Later among the works it cites.
DeepMatch: Balancing deep covariate representations for causal inference using adversarial training
Kallus, N. (2020) · 2020
Later among the works it cites.
Characterizing optimal mixed policies: Where to intervene and what to observe
Lee, S. and Bareinboim, E. (2020) · 2020
Later among the works it cites.
Adversarial balancing-based representation learning for causal effect inference with observational data
Du, X., Sun, L., Duivesteijn, W., Nikolaev, A., and Pechenizkiy, M. (2021) · 2021
Closest in time.
Generalization bounds and representation learning for estimation of potential outcomes and causal effects
Johansson, F. D., Shalit, U., Kallus, N., and Sontag, D. (2021) · 2021
Closest in time.
Estimating identifiable causal effects through double machine learning
Jung, Y., Tian, J., and Bareinboim, E. (2021) · 2021
Closest in time.
Semiparametric counterfactual density estimation
Kennedy, E. H., Balakrishnan, S., and Wasserman, L. (2021) · 2021
Closest in time.
The Causal-Neural Connection: Expressiveness, Learnability, Inference
Xia, K., Lee, K.-Z., Bengio, Y., and Bareinboim, E. (2021) · 2021
Closest in time.
Non-Parametric Methods for Partial Identification of Causal Effects
Zhang, J. and Bareinboim, E. (2021) · 2021
Closest in time.
Partial Identification of Counterfactual Distributions
Zhang, J., Tian, J., and Bareinboim, E. (2021) · 2021
Closest in time.