暂无分享,去创建一个
[1] Aldo A. Faisal,et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care , 2018, Nature Medicine.
[2] S. Cole,et al. Time-modified confounding. , 2009, American journal of epidemiology.
[3] Bryan Lim,et al. Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks , 2018, NeurIPS.
[4] Ilias Tagkopoulos,et al. From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis , 2015, JMIR medical informatics.
[5] Neil D. Lawrence,et al. Bayesian Gaussian Process Latent Variable Model , 2010, AISTATS.
[6] Max Welling,et al. Causal Effect Inference with Deep Latent-Variable Models , 2017, NIPS 2017.
[7] G. Imbens,et al. Comment on: “The Blessings of Multiple Causes” by Yixin Wang and David M. Blei , 2019, Journal of the American Statistical Association.
[8] Ian R White,et al. Adjusting for partially missing baseline measurements in randomized trials , 2005, Statistics in medicine.
[9] L. Rüschendorf. On the distributional transform, Sklar's theorem, and the empirical copula process , 2009 .
[10] Thomas Plümper,et al. Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects , 2007, Political Analysis.
[11] George Hripcsak,et al. The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records , 2019, MLHC.
[12] Mihaela van der Schaar,et al. Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders , 2019, ICML.
[13] Steven L. Scott,et al. Inferring causal impact using Bayesian structural time-series models , 2015, 1506.00356.
[14] Suchi Saria,et al. Learning Treatment-Response Models from Multivariate Longitudinal Data , 2017, UAI.
[15] David M. Blei,et al. The Deconfounded Recommender: A Causal Inference Approach to Recommendation , 2018, ArXiv.
[16] J. Robins. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .
[17] Uri Shalit,et al. Learning Representations for Counterfactual Inference , 2016, ICML.
[18] Vikash K. Mansinghka,et al. Causal Inference using Gaussian Processes with Structured Latent Confounders , 2020, ICML.
[19] Kris K. Hauser,et al. Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach , 2013, Artif. Intell. Medicine.
[20] Sergey Levine,et al. Offline policy evaluation across representations with applications to educational games , 2014, AAMAS.
[21] Peter Szolovits,et al. MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.
[22] O. Kallenberg. Foundations of Modern Probability , 2021, Probability Theory and Stochastic Modelling.
[23] D. Rubin. Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician , 1984 .
[24] Dustin Tran,et al. Implicit Causal Models for Genome-wide Association Studies , 2017, ICLR.
[25] Suchi Saria,et al. Reliable Decision Support using Counterfactual Models , 2017, NIPS.
[26] Fredrik D. Johansson,et al. Guidelines for reinforcement learning in healthcare , 2019, Nature Medicine.
[27] M. Sklar. Fonctions de repartition a n dimensions et leurs marges , 1959 .
[28] Stefan Wager,et al. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests , 2015, Journal of the American Statistical Association.
[29] Neil D. Lawrence,et al. Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.
[30] Eric J. Tchetgen Tchetgen,et al. Comment on “Blessings of Multiple Causes” , 2019, Journal of the American Statistical Association.
[31] Bernhard Schölkopf,et al. Deconfounding Reinforcement Learning in Observational Settings , 2018, ArXiv.
[32] Jennifer L. Hill,et al. Assessing lack of common support in causal inference using bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes , 2013, 1311.7244.
[33] Stefan Feuerriegel,et al. Early Detection of User Exits from Clickstream Data: A Markov Modulated Marked Point Process Model , 2020, WWW.
[34] David M. Blei,et al. The Blessings of Multiple Causes , 2018, Journal of the American Statistical Association.
[35] Mihaela van der Schaar,et al. Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes , 2017, NIPS.
[36] Mihaela van der Schaar,et al. Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations , 2020, ICLR.
[37] J. Robins,et al. Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.
[38] Stefan Feuerriegel,et al. AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units , 2021, KDD.
[39] Brian T. Denton,et al. Markov decision processes for screening and treatment of chronic diseases , 2017 .
[40] Zhichao Jiang,et al. Discussion of "The Blessings of Multiple Causes" by Wang and Blei , 2019, 1910.06991.
[41] Uri Shalit,et al. Estimating individual treatment effect: generalization bounds and algorithms , 2016, ICML.
[42] Donald B. Rubin,et al. Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .
[43] Kieran R. Campbell,et al. Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models , 2018, ICML.
[44] Stefan Feuerriegel,et al. Estimating Average Treatment Effects via Orthogonal Regularization , 2021, CIKM.
[45] M. Robins James,et al. Estimation of the causal effects of time-varying exposures , 2008 .
[46] Alexander D'Amour,et al. On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative , 2019, AISTATS.
[47] N. Adler,et al. Patients in context--EHR capture of social and behavioral determinants of health. , 2015, The New England journal of medicine.
[48] Kitty S. Chan,et al. Review: Electronic Health Records and the Reliability and Validity of Quality Measures: A Review of the Literature , 2010, Medical care research and review : MCRR.
[49] Neil D. Lawrence,et al. Gaussian Processes for Big Data , 2013, UAI.
[50] Mihaela van der Schaar,et al. Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms , 2021, AISTATS.
[51] David M. Blei,et al. The Blessings of Multiple Causes: Rejoinder , 2019, Journal of the American Statistical Association.
[52] Michael N Cantor,et al. Integrating Data On Social Determinants Of Health Into Electronic Health Records. , 2018, Health affairs.