GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets

By Jinsung Yoon, James Jordon, Mihaela van der Schaar. 2018

473 citations recorded by the source corpus.

Subfields: Generative Models, Causal Inference & Discovery

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “While it may be useful to automate certain decisions, an understanding of causality may be necessary to recommend treatment options that are personalized and reliable [3], [6], [31], [164], [201], [224], [242], [273].”
Time-series forecasting with deep learning: a survey (2020, background): “rom a forecasting perspective, i.e. determining which actions to take to optimise future outcomes. While a large class of deep learning methods exists for estimating causal effects in static settings [77, 78, 79], the key challenge in time series datasets is the presence of time-dependent confoundin”
Towards Causal Representation Learning (2021, background): “While it may be useful to automate certain decisions, an understanding of causality may be necessary to recommend treatment options that are personalized and reliable [201, 242, 224, 273, 6, 3, 30, 165].”
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Most of the previous methods for performing causal inference have focused on the static setting (Hill, 2011; Wager & Athey, 2017; Alaa & van der Schaar, 2017; Yoon et al., 2018), and less attention has been given to the time series setting.”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “…inference using observational data in the static setting (Johansson et al., 2016; Shalit et al., 2017; Alaa & van der Schaar, 2017; Li & Fu, 2017; Yoon et al., 2018; Alaa & van der Schaar, 2018; Yao et al., 2018) and only a few methods address the longitudinal setting (Xu et al., 2016; Roy et…”
Representation Balancing MDPs for Off-Policy Policy Evaluation (2018, background): “Recently, different kinds of machine learning97 models such as Gaussian Processes [1], random forests [24], and GANs [8] have been used to estimate98 heterogeneous treatment effects (HTE), in non-sequential settings.”
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks (2018, methodology): “Deep learning has also been used to estimate individualized treatment effects for a single intervention at a fixed time, using instrumental variable approaches [13], generative adversarial networks [44] and multi-task architectures [3].”
Adapting Neural Networks for the Estimation of Treatment Effects (2019, unknown): “CEVEA [ Lou + 17 ] , GANITE [ Yoo + 18 ] , and CMPGP [ AS17 ] use VAEs, GANs, and multi-task gaussian processes, respectively, to estimate treatment effects.”
Counterfactual VQA: A Cause-Effect Look at Language Bias (2020, background): “The machine learning community has also made efforts in treatment effect estimation [39, 40, 41] and real-world applications [42, 43, 44, 28, 45, 46, 47].”
A Survey on Causal Inference (2020, methodology): “ed (the heavier one in the twin) and control (the lighter one in the twin) outcomes are observed. The treatment assignment usually defined by the users to simulate the selection bias. For example, in [81, 152], the selection bias is created by the following procedure:W i|X i ∼Bern(Siдmoid(w ′X i)+n)”
Appendix for “Learning Overlapping Representations for the Estimation of Individualized Treatment Effects” (2020, background): “In DKLITE, the negative log marginal likelihood L (Θ t ) can be rewritten as, where the second equality is achieved by [1,8,14,18,19].”
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 (2020, methodology): “They can anticipate a patient’s future healthcare needs, and can also handle irregularly collected data and impute missing features (Yoon et al. 2018a, b), as well as inferring important predictors of patient outcomes (e.g. specific comorbidities that are more likely to worsen the prognosis of a…”
GAIN: Missing Data Imputation using Generative Adversarial Nets (2018, methodology): “Copyright 2018 by the author(s). heart rate of the patient (Yoon et al., 2018c).”
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020, methodology): “While the job of the counterfactual generator is to generate outcomes for the treatment-dosage pairs which were not observed, [6] demonstrated that the performance of the counterfactual generator is improved by adding a supervised loss term that regularises its output for the factual treatment (in o”