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Using observational data to estimate the effect of a treatment is a powerful tool for decision-making when randomized experiments are infeasible or costly.
Bayesian inference for causal effects: The role of randomization
D. B. Rubin · 1978
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Bayesianly justifiable and relevant frequency calculations for the applies statistician
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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
J. Robins · 1986
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Marginal structural models and causal inference in epidemiology
J. M. Robins, M. A. Hernán, and B. Brumback · 2000
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Gaussian process latent variable models for visualisation of high dimensional data
N. D. Lawrence · 2004
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Causal inference using potential outcomes: Design, modeling, decisions
D. B. Rubin · 2005
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Adjusting for partially missing baseline measurements in randomized trials
I. R. White and S. G. Thompson · 2005
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Foundations of modern probability
O. Kallenberg · 2006
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Efficient estimation of time-invariant and rarely changing variables in finite sample panel analyses with unit fixed effects
T. Plümper and V. E. Troeger · 2007
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Estimation of the causal effects of time-varying exposures
J. M. Robins and M. A. Hernán · 2009
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Electronic health records and the reliability and validity of quality measures: a review of the literature
K. S. Chan, J. B. Fowles, and J. P. Weiner · 2010
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Bayesian gaussian process latent variable model
M. Titsias and N. D. Lawrence · 2010
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Artificial intelligence framework for simulating clinical decision-making: A markov decision process approach
C. C. Bennett and K. Hauser · 2013
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Gaussian processes for big data
J. Hensman, N. Fusi, and N. D. Lawrence · 2013
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Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children’s cognitive outcomes
J. L. Hill and Y. S. Su · 2013
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Offline policy evaluation across representations with applications to educational games
T. Mandel, Y.-E. Liu, S. Levine, E. Brunskill, and Z. Popovic · 2014
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Patients in context—ehr capture of social and behavioral determinants of health
N. E. Adler and W. W. Stead · 2015
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Inferring causal impact using Bayesian structural time-series models
K. H. Brodersen, F. Gallusser, J. Koehler, N. Remy, and S. L. Scott · 2015
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From data to optimal decision making: a data-driven, probabilistic machine learning approach to decision support for patients with sepsis
A. Tsoukalas, T. Albertson, and I. Tagkopoulos · 2015
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Learning representations for counterfactual inference
F. D. Johansson, U. Shalit, and D. Sontag · 2016
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Mimic-iii, a freely accessible critical care database
The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care
M. Komorowski, L. A. Celi, O. Badawi, A. C. Gordon, and A. A. Faisal · 2018
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Forecasting treatment responses over time using recurrent marginal structural networks
B. Lim, A. M. Alaa, and M. van der Schaar · 2018
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Deconfounding reinforcement learning in observational settings
C. Lu, B. Schölkopf, and J. M. Hernández-Lobato · 2018
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Estimation and inference of heterogeneous treatment effects using random forests
S. Wager and S. Athey · 2018
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The deconfounded recommender: A causal inference approach to recommendation
Y. Wang, D. Liang, L. Charlin, and D. M. Blei · 2018
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T. J. Johnson, A. E.and Pollard, L. Shen, H. L. Li-Wei, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. A. Celi, and R. G. Mark · 2016
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Bayesian inference of individualized treatment effects using multi-task Gaussian processes
A. M. Alaa and M. van der Schaar · 2017
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Causal effect inference with deep latent-variable models
C. Louizos, U. Shalit, J. Mooij, D. Sontag, R. Zemel, and M. Welling · 2017
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Reliable decision support using counterfactual models
P. Schulam and S. Saria · 2017
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Estimating individual treatment effect: Generalization bounds and algorithms
U. Shalit, F. D. Johansson, and D. Sontag · 2017
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Treatment-response models for counterfactual reasoning with continuous-time, continuous-valued interventions
H. Soleimani, A. Subbaswamy, and S. Saria · 2017
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Markov decision processes for screening and treatment of chronic diseases
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Comment on: “the blessings of multiple causes” by yixin wang and david m. blei
S. Athey, G. W. Imbens, and M. Pollmann · 2019
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On multi-cause approaches to causal inference with unobserved counfounding: Two cautionary failure cases and a promising alternative
A. D’Amour · 2019
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Guidelines for reinforcement learning in healthcare
O. Gottesman, F. Johansson, M. Komorowski, A. Faisal, D. Sontag, F. Doshi-Velez, and L. A. Celi · 2019
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Discussion of "the blessings of multiple causes" by wang and blei
K. Imai and Z. Jiang · 2019
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Decomposing feature-level variation with covariate gaussian process latent variable models
K. Märtens, K. Campbell, and C. Yau · 2019
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Comment on “blessings of multiple causes”
E. L. Ogburn, I. Shpitser, and E. J. T. Tchetgen · 2019
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The medical deconfounder: Assessing treatment effects with electronic health records
L. Zhang, Y. Wang, A. Ostropolets, J. J. Mulgrave, D. M. Blei, and G. Hripcsak · 2019
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Towards clarifying the theory of the deconfounder
Y. Wang and D. M. Blei · 2020
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Causal inference using gaussian processes with structured latent confounders
S. Witty, K. Takatsu, D. Jensen, and V. Mansinghka · 2020
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