When causality matters for prediction

7 years 11 months ago
When causality matters for prediction
Recent evaluations have indicated that in practice, general methods for prediction which do not account for changes in the conditional distribution of a target variable given feature values in some cases outperform causal discovery based methods for prediction which can account for such changes. We investigate some possibilities which may explain these findings. We give theoretical conditions, which are confirmed experimentally, for when particular manipulations of variables should not affect predictions for a target. We then consider the tradeoff between errors related to causality, i.e. not accounting for changes in a distribution after variables are manipulated, and errors resulting from sample bias, overfitting, and assuming specific parametric forms that do not fit the data, which most existing causal discovery based methods are particularly prone to making.
Robert E. Tillman, Peter Spirtes
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Robert E. Tillman, Peter Spirtes
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