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In medical practice, treatments are selected based on the expected causal effects on patient outcomes.
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A new approach to causal inference in mortality studies with a sustained exposure period: Application to control of the healthy worker survivor effect
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Causal inference in public health
Glass, T. A.; Goodman, S. N.; Hernán, M. A.; and Samet, J. M. 2013 · 2013
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Kingma, D. P.; and Ba, J. 2015 · 2015
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Causal inference in the age of decision medicine
Yazdani, A. M.; and Boerwinkle, E. 2015 · 2015
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Gal, Y.; and Ghahramani, Z. 2016 · 2016
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MIMIC-III, a freely accessible critical care database
Johnson, A. E. W.; Pollard, T. J.; Shen, L.; Lehman, L.-w. H.; Feng, M.; Ghassemi, M.; Moody, B.; Szolovits, P.; Celi, L. A.; and Mark, R. G. 2016 · 2016
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Semiparametric theory and empirical processes in causal inference
Kennedy, E. H. 2016 · 2016
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Forecasting treatment responses over time using recurrent marginal structural networks
Lim, B.; Alaa, A. M.; and van der Schaar, M. 2018 · 2018
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Estimation and inference of heterogeneous treatment effects using random forests
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Bica, I.; Alaa, A. M.; Jordon, J.; and van der Schaar, M. 2020 · 2020
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Time series deconfounder: Estimating treatment effects over time in the presence of hidden confounders
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Revisiting g-estimation of the effect of a time-varying exposure subject to time-varying confounding
Vansteelandt, S.; and Sjolander, A. 2016 · 2016
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Causal inference in economics and marketing
Varian, H. R. 2016 · 2016
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An introduction to g methods
Naimi, A. I.; Cole, S. R.; and Kennedy, E. H. 2017 · 2017
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Identifying subgroups of patients using latent class analysis: Should we use a single-stage or a two-stage approach? A methodological study using a cohort of patients with low back pain
Nielsen, A. M.; Kent, P.; Hestbaek, L.; Vach, W.; and Kongsted, A. 2017 · 2017
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Reliable decision support using counterfactual models
Schulam, P.; and Saria, S. 2017 · 2017
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Estimating individual treatment effect: Generalization bounds and algorithms
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Bica, I.; Alaa, A. M.; and van der Schaar, M. 2020 · 2020
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Estimating individual treatment effects with time-varying confounders
Liu, R.; Yin, C.; and Zhang, P. 2020 · 2020
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gfoRmula: An R package for estimating the effects of sustained treatment strategies via the parametric G-formula
McGrath, S.; Lin, V.; Zhang, Z.; Petito, L. C.; Logan, R. W.; Hernán, M. A.; and Young, J. G. 2020 · 2020
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MIMIC-extract: A data extraction, preprocessing, and representation pipeline for MIMIC-III
Wang, S.; McDermott, M. B.; Chauhan, G.; Ghassemi, M.; Hughes, M. C.; and Naumann, T. 2020 · 2020
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Analyzing patient trajectories with artificial intelligence
Allam, A.; Feuerriegel, S.; Rebhan, M.; and Krauthammer, M. 2021 · 2021
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Disentangled counterfactual recurrent networks for treatment effect inference over time
Berrevoets, J.; Curth, A.; Bica, I.; McKinney, E.; and van der Schaar, M. 2021 · 2021
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G-Net: A recurrent network approach to G-computation for counterfactual prediction under a dynamic treatment regime
Li, R.; Hu, S.; Lu, M.; Utsumi, Y.; Chakraborty, P.; Sow, D. M.; Madan, P.; Li, J.; Ghalwash, M.; Shahn, Z.; and Lehman, L.-w. 2021 · 2021
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SyncTwin: Treatment effect estimation with longitudinal outcomes
Qian, Z.; Zhang, Y.; Bica, I.; Wood, A. M.; and van der Schaar, M. 2021 · 2021
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Estimating individual treatment effects under unobserved confounding using binary instruments
Frauen, D.; and Feuerriegel, S. 2022 · 2022
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