Reliable Decision Support using Counterfactual Models

By Peter F. Schulam, Suchi Saria. 2017

227 citations recorded by the source corpus.

Subfields: Causal Inference & Discovery, Medical & Health AI

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Citations in the corpus

Neural Ordinary Differential Equations (2018, neutral): “GPs have been adapted to ﬁ t differential equations (Raissi et al., 2018b) and can naturally model continuous-time effects and interventions (Soleimani et al., 2017b; Schulam and Saria, 2017).”
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].”
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].”
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “While GPs have previously been studied in a causal context for structure learning [13, 56], estimating treatment effects [2, 43], or learning SCMs with latent variables and measurement error [47], our goal here is to account for the uncertainty over fr in the computation of the posterior over Ur, an”
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “…to learn individualized treatment responses from longitudinal disease trajectories containing information about patient covariates and treatments assignments over time (Robins, 1986; Robins et al., 2000; Robins & Hernán, 2008; Soleimani et al., 2017; Schulam & Saria, 2017; Lim et al., 2018).”
Deconfounding Reinforcement Learning in Observational Settings (2018, background): “Causal inference is a fundamental notion in science and plays an increasingly important role in healthcare and medicine (Liu et al.; Soleimani et al., 2017; Schulam and Saria, 2017; Alaa et al., 2017; Alaa and van der Schaar, 2018; Atan et al., 2016).”
Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms (2018, background): “Specifically, they optimize the counterfactual risk to explicitly account for variations in policy between train and test environments (e.g., Swaminathan and Joachims (2015); Schulam and Saria (2017)).”
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes (2020, methodology): “This is not straightforward to do with history adjusted MSMs. Schulam and Saria [22] propose Counterfactual Gaussian Processes, an implementation of continuous time gcomputation, and like us apply their method to ICU data.”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “In real datasets, counterfactual outcomes and the degree of time-dependent confounding are not known (Schulam & Saria, 2017; Lim et al., 2018).”
Learning When-to-Treat Policies (2019, background): “, 2012) and Gaussian processes (Schulam and Saria, 2017) have been proposed for the off-policy setting as well.”
Representation Balancing MDPs for Off-Policy Policy Evaluation (2018, methodology): “Schulam and Saria [18] study99 using Gaussian process models for treatment effect estimation in continuous time settings.”
A Review of Challenges and Opportunities in Machine Learning for Health. (2018, background): “Many of the most important and exciting problems in healthcare require algorithms that can answer causal , “what if?'' questions about what will happen if a doctor administers a treatment 11 .”
AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “Researchers can also reduce bias during the model building process [133] using methods such as counterfactual Gaussian Process which is developed to perform both risk prediction and conduct “what-if ” reasoning for individualized treatment planning.”
A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury (2019, unknown): “Future work will need to address the under-representation of sub-populations in the training data 28 and overcome the impact of potential confounding factors related to hospital processes 29 .”
Opportunities in Machine Learning for Healthcare (2018, unknown): “ay in turn suggest alternative treatment strategies (Alge and Arthur, 2014). By personalizing over time, one can learn individualspeciﬁctreatmenteffectsthataddressthecauseofthesyndrome(Xu et al.,2016;Schulam and Saria, 2017) in a particular individual. This relates to the ideas from “N =1” crossover”
Learning Treatment-Response Models from Multivariate Longitudinal Data (2017, background): “…worth noting that in the potential outcomes framework, estimation of the causal effect is precluded by the need to justify a set of generally untestable assumptions (Gill and Robins, 2001); e.g., see Lok (2008); Schulam and Saria (2017) for assumptions needed for continuoustime potential outcomes.”
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming (2019, methodology): “This has seen counterfactuals applied to medicine (Constantinou et al., 2016; Schulam and Saria, 2017; Richens et al., 2019; Oberst and Sontag, 2019), advertisement and search (Bottou et al.”
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models (2019, methodology): “However, this is often used (e.g., Schulam & Saria, 2017; Johansson et al., 2016) primarily towards predicting quantities related to the interventional distribution, such as CATE.”