Assessing sensitivity of machine learning predictions. a novel toolbox with an application to financial literacy
Despite their popularity, machine learning predictions are sensitive to potential unobserved
predictors. This paper proposes a general algorithm that assesses how the omission of an
unobserved variable with high explanatory power could affect the predictions of the model.
Moreover, the algorithm extends the usage of machine learning from pointwise predictions
to inference and sensitivity analysis. In the application, we show how the framework can be
applied to data with inherent uncertainty, such as students' scores in a standardized …
predictors. This paper proposes a general algorithm that assesses how the omission of an
unobserved variable with high explanatory power could affect the predictions of the model.
Moreover, the algorithm extends the usage of machine learning from pointwise predictions
to inference and sensitivity analysis. In the application, we show how the framework can be
applied to data with inherent uncertainty, such as students' scores in a standardized …
Assessing sensitivity of machine learning predictions. A novel toolbox with an application to financial literacy
FJ Bargagli Stoffi, K De Beckker… - arXiv e …, 2021 - ui.adsabs.harvard.edu
Despite their popularity, machine learning predictions are sensitive to potential unobserved
predictors. This paper proposes a general algorithm that assesses how the omission of an
unobserved variable with high explanatory power could affect the predictions of the model.
Moreover, the algorithm extends the usage of machine learning from pointwise predictions
to inference and sensitivity analysis. In the application, we show how the framework can be
applied to data with inherent uncertainty, such as students' scores in a standardized …
predictors. This paper proposes a general algorithm that assesses how the omission of an
unobserved variable with high explanatory power could affect the predictions of the model.
Moreover, the algorithm extends the usage of machine learning from pointwise predictions
to inference and sensitivity analysis. In the application, we show how the framework can be
applied to data with inherent uncertainty, such as students' scores in a standardized …