A scoping review of causal methods enabling predictions under hypothetical interventions

• URL: http://arxiv.org/abs/2011.09815v2
• Date: Tue, 12 Jan 2021 15:31:04 GMT
• Title: A scoping review of causal methods enabling predictions under hypothetical interventions
• Authors: Lijing Lin, Matthew Sperrin, David A. Jenkins, Glen P. Martin, Niels Peek
• Abstract summary: When prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions. We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used for predictions under hypothetical interventions. There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models.
• Score: 4.801185839732629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background and Aims: The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions. We aimed to identify published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference: their main methodological approaches, underlying assumptions, targeted estimands, and potential pitfalls and challenges with using the method, and unresolved methodological challenges. Methods: We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used for predictions under hypothetical interventions. Results: We identified 4919 papers through database searches and a further 115 papers through manual searches, of which 13 were selected for inclusion, from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation. Conclusions: There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models: 1) enriching prediction models derived from observational studies with estimated causal effects from clinical trials and meta-analyses; and 2) estimating prediction models and causal effects directly from observational data. These methods require extending to dynamic treatment regimes, and consideration of multiple interventions to operationalise a clinical decision support system. Techniques for validating 'causal prediction models' are still in their infancy.

Related papers

• Predicting from Predictions [18.393971232725015]
  We study how causal effects of predictions on outcomes can be identified from observational data. We show that supervised learning that predict from predictions can find transferable functional relationships between features, predictions, and outcomes.
  arXiv  Detail & Related papers  (2022-08-15T16:57:02Z)
• Boosting the interpretability of clinical risk scores with intervention predictions [59.22442473992704]
  We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions. We show how combining typical risk scores, such as the likelihood of mortality, with future intervention probability scores leads to more interpretable clinical predictions.
  arXiv  Detail & Related papers  (2022-07-06T19:49:42Z)
• Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning [31.97813934144506]
  We introduce a new representation learning framework, which considers the provision of counterfactual explanations as an embedded property of the risk model. Our results suggest that our proposed framework has the potential to help researchers and clinicians improve personalised care.
  arXiv  Detail & Related papers  (2022-05-15T09:41:16Z)
• Causality and Generalizability: Identifiability and Learning Methods [0.0]
  This thesis contributes to the research areas concerning the estimation of causal effects, causal structure learning, and distributionally robust prediction methods. We present novel and consistent linear and non-linear causal effects estimators in instrumental variable settings that employ data-dependent mean squared prediction error regularization. We propose a general framework for distributional robustness with respect to intervention-induced distributions.
  arXiv  Detail & Related papers  (2021-10-04T13:12:11Z)
• Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction [9.199022926064009]
  The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. We present a method to provide people with clinical expertise with domain-relevant evidence about why a prediction should be trusted.
  arXiv  Detail & Related papers  (2021-03-04T00:26:32Z)
• Bayesian prognostic covariate adjustment [59.75318183140857]
  Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates.
  arXiv  Detail & Related papers  (2020-12-24T05:19:03Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)
• Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model [71.9860741092209]
  Clinical researchers often select among and evaluate risk prediction models. Standard metrics calculated from retrospective data are only related to model utility under certain assumptions. When predictions are delivered repeatedly throughout time, the relationship between standard metrics and utility is further complicated.
  arXiv  Detail & Related papers  (2020-06-02T16:26:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.