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Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions.
Spectra of some self-exciting and mutually exciting point processes
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Effect of physical activity on functional performance and knee pain in patients with osteoarthritis: analysis with marginal structural models
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Changes in volume of stage i non-small-cell lung cancer during stereotactic body radiotherapy
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Learning representations for counterfactual inference
Johansson, F., Shalit, U., and Sontag, D · 2016
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Hawkes processes with stochastic excitations
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A flexible parametric approach for estimating continuous-time inverse probability of treatment and censoring weights
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Ganite: Estimation of individualized treatment effects using generative adversarial nets
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Bayesian inference of individualized treatment effects using multi-task gaussian processes
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Learning from clinical judgments: Semi-markov-modulated marked hawkes processes for risk prognosis
Alaa, A. M., Hu, S., and Schaar, M · 2017
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Hawkes process modeling of adverse drug reactions with longitudinal observational data
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Melanoma staging: evidence-based changes in the american joint committee on cancer eighth edition cancer staging manual
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The additive hazard estimator is consistent for continuous-time marginal structural models
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The blessings of multiple causes
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Estimating counterfactual treatment outcomes over time through adversarially balanced representations
Bica, I., Alaa, A. M., Jordon, J., and van der Schaar, M · 2020
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Neural ordinary differential equations for intervention modeling
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Neural controlled differential equations for irregular time series
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Approximation capabilities of neural ODEs and invertible residual networks
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Policy analysis using synthetic controls in continuous-time
Bellot, A. and van der Schaar, M · 2021
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From real-world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
Bica, I., Alaa, A. M., Lambert, C., and Van Der Schaar, M · 2021
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Neural controlled differential equations for online prediction tasks
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