Causal Bootstrapping

Ever since Judea Pearl's book came out over a year ago, there has been a renewed interested in causal reasoning in the field. This is one of the most substantive pieces of work I've seen actually done on the topic.

Here, we develop causal bootstrapping, a set of techniques for augmenting classical nonparametric bootstrap resampling with information about the causal relationship between variables. This makes it possible to resample observational data such that, if it is possible to identify an interventional relationship from that data, new data representing that relationship can be simulated from the original observational data. In this way, we can use modern machine learning algorithms unaltered to make statistically powerful, yet causally-robust, predictions.

Dense, and early. This is an area worth watching though.


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