Fetching the paper…
Reading the bibliography…
A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns.
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. 1974 · 1974
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M.; and Cohen, N. J. 1989 · 1989
Earlier work this paper cites.
On the application of probability theory to agricultural experiments. Essay on principles. Section 9
Splawa-Neyman, J.; Dabrowska, D. M.; and Speed, T. 1990 · 1990
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
French, R. M. 1999 · 1999
Earlier work this paper cites.
Incremental learning of privacy-preserving Bayesian networks
Samet, S.; Miri, A.; and Granger, E. 2013 · 2013
Earlier work this paper cites.
Causal inference in statistics, social, and biomedical sciences
Imbens, G. W.; and Rubin, D. B. 2015 · 2015
Earlier work this paper cites.
Recursive partitioning for heterogeneous causal effects
Athey, S.; and Imbens, G. 2016 · 2016
Earlier work this paper cites.
Matching on balanced nonlinear representations for treatment effects estimation
Li, S.; and Fu, Y. 2017 · 2017
Earlier work this paper cites.
Causal effect inference with deep latent-variable models
Louizos, C.; Shalit, U.; Mooij, J. M.; Sontag, D.; Zemel, R.; and Welling, M. 2017 · 2017
Cited alongside, same era.
Estimating individual treatment effect: generalization bounds and algorithms
Shalit, U.; Johansson, F. D.; and Sontag, D. 2017 · 2017
Cited alongside, same era.
Estimation and inference of heterogeneous treatment effects using random forests
Wager, S.; and Athey, S. 2018 · 2018
Cited alongside, same era.
Representation learning for treatment effect estimation from observational data
Yao, L.; Li, S.; Li, Y.; Huai, M.; Gao, J.; and Zhang, A. 2018 · 2018
Cited alongside, same era.
GANITE: Estimation of individualized treatment effects using generative adversarial nets
Yoon, J.; Jordon, J.; and van der Schaar, M. 2018 · 2018
Cited alongside, same era.
Metalearners for estimating heterogeneous treatment effects using machine learning
Class-incremental learning via deep model consolidation
Zhang, J.; Zhang, J.; Ghosh, S.; Li, D.; Tasci, S.; Heck, L.; Zhang, H.; and Kuo, C.-C. J. 2020 · 2020
Later among the works it cites.
Graph infomax adversarial learning for treatment effect estimation with networked observational data
Chu, Z.; Rathbun, S. L.; and Li, S. 2021 · 2021
Later among the works it cites.
Quasi-oracle estimation of heterogeneous treatment effects
Nie, X.; and Wager, S. 2021 · 2021
Later among the works it cites.
A survey on causal inference
Yao, L.; Chu, Z.; Li, S.; Li, Y.; Gao, J.; and Zhang, A. 2021 · 2021
Later among the works it cites.
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data
Chu, Z.; Rathbun, S. L.; and Li, S. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Künzel, S. R.; Sekhon, J. S.; Bickel, P. J.; and Yu, B. 2019 · 2019
Cited alongside, same era.
Unbiased scene graph generation from biased training
Tang, K.; Niu, Y.; Huang, J.; Shi, J.; and Zhang, H. 2020 · 2020
Cited alongside, same era.
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities
Chu, Z.; Huang, J.; Li, R.; Chu, W.; and Li, S. 2023a
Cited in the paper.
Continual Causal Inference with Incremental Observational Data
Chu, Z.; Li, R.; Rathbun, S.; and Li, S. 2023b
Cited in the paper.
Continual Lifelong Causal Effect Inference with Real World Evidence
Chu, Z.; Rathbun, S.; and Li, S. 2020a
Cited in the paper.
Matching in selective and balanced representation space for treatment effects estimation
Chu, Z.; Rathbun, S. L.; and Li, S. 2020b
Cited in the paper.
Show, Deconfound and Tell: Image Captioning With Causal Inference
Liu, B.; Wang, D.; Yang, X.; Zhou, Y.; Yao, R.; Shao, Z.; and Zhao, J. 2022a
Cited in the paper.
Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
Feder, A.; Keith, K. A.; Manzoor, E.; Pryzant, R.; Sridhar, D.; Wood-Doughty, Z.; Eisenstein, J.; Grimmer, J.; Reichart, R.; Roberts, M. E.; et al. 2022 · 2022
Later among the works it cites.
Learning causal effects on hypergraphs
Ma, J.; Wan, M.; Yang, L.; Li, J.; Hecht, B.; and Teevan, J. 2022 · 2022
Later among the works it cites.