Counterfactual Prediction Statement

A Counterfactual Prediction Statement is a prediction statement that is a counterfactual statement.

• See: Counterfactual Query, Prediction, Counterfactual Inference.

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References

2020

• (Prosperi et al., 2020) ⇒ Mattia Prosperi, Yi Guo, Matt Sperrin, James S. Koopman, Jae S. Min, Xing He, Shannan Rich, Mo Wang, Iain E. Buchan, and Jiang Bian. (2020). “Causal Inference and Counterfactual Prediction in Machine Learning for Actionable Healthcare.” Nature Machine Intelligence, 2(7).
  • QUOTE: Big data, high-performance computing, and (deep) machine learning are increasingly becoming key to precision medicine—from identifying disease risks and taking preventive measures, to making diagnoses and personalizing treatment for individuals. Precision medicine, however, is not only about predicting risks and outcomes, but also about weighing interventions. Interventional clinical predictive models require the correct specification of cause and effect, and the calculation of so-called counterfactuals, that is, alternative scenarios. In biomedical research, observational studies are commonly affected by confounding and selection bias. Without robust assumptions, often requiring a priori domain knowledge, causal inference is not feasible. Data-driven prediction models are often mistakenly used to draw causal effects, but neither their parameters nor their predictions necessarily have a causal interpretation. Therefore, the premise that data-driven prediction models lead to trustable decisions/interventions for precision medicine is questionable. When pursuing intervention modelling, the bio-health informatics community needs to employ causal approaches and learn causal structures. Here we discuss how target trials (algorithmic emulation of randomized studies), transportability (the licence to transfer causal effects from one population to another) and prediction invariance (where a true causal model is contained in the set of all prediction models whose accuracy does not vary across different settings) are linchpins to developing and testing intervention models.

2017

• (Hartford et al., 2017) ⇒ Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. (2017). “Deep IV: A Flexible Approach for Counterfactual Prediction.” In: International Conference on Machine Learning, pp. 1414-1423 . PMLR,
  • ABSTRACT: Counterfactual prediction requires understanding causal relationships between so-called [[treatment variable|treatment] and outcome variables. This paper provides a recipe for augmenting deep learning methods to accurately characterize such relationships in the presence of instrument variables (IVs) – sources of treatment randomization that are conditionally independent from the outcomes. Our IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves integration over the conditional treatment distribution. This Deep IV framework allows us to take advantage of off-the-shelf supervised learning techniques to estimate causal effects by adapting the loss function. Experiments show that it outperforms existing machine learning approaches.

2019

• https://arithmox.ai/counterfactual-prediction-the-most-important-task-for-data-scientists/
  • QUOTE: ... The idea of counterfactual prediction is to consider output in a hypothetical setting where some features of today’s world were changed. Heuristically, scientific questions based on counterfactual predictions can be phrased as What would happen if questions. For example, we may aim to predict what would happen to the number of visits on our website if we updated the layout. Or we may aim to predict what would happen to the number of items sold, if we bought an online advertisement.

    The idea of counterfactual prediction is to consider output in a hypothetical setting where some features of today’s world were changed. Heuristically, scientific questions based on counterfactual predictions can be phrased as What would happen if questions. For example, we may aim to predict what would happen to the number of visits on our website if we updated the layout. Or we may aim to predict what would happen to the number of items sold, if we bought an online advertisement. …

2007

• (Olszewski & Sandroni, 2007) ⇒ Wojciech Olszewski, and Alvaro Sandroni. (2007). “Counterfactual Predictions."

2007

• (King & Zeng, 2007) ⇒ Gary King, and Langche Zeng. (2007). “When Can History Be Our Guide? The Pitfalls of Counterfactual Inference". International Studies Quarterly, 51(1). doi:10.1111/j.1468-2478.2007.00445.x

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