Most of the literature in fair machine learning focuses on defining and achieving fairness criteria in the context of prediction, while not explicitly focusing on how these predictions may be used later on in the pipeline.

The study of causal relations can be seen through the lens of learning andinference [16,21].

Based on the condition of alcohol dependant patients (S1), the physician may prescribe a medication or behavioral therapy (X1).

One of the most common mistakes made when performing data analysis is attributing causal meaning toregression coefficients.

In the causal inference literature, this generalization task has been formalized under the rubric oftransportability (Pearl and Bareinboim, 2011), where a number of criteria and algorithms have been developed forvarious settings.

Let G beacausalQ = Pr.

The model proposed here enables proper uncertainty quantification of the causal effects thus allowing the definition of optimal experimental design strategies.

In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically,building onamore explicit definition ofnested counterfactuals, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.

Recent efforts for developing general-purpose estimators with broader coverage, incorporating thefront-door adjustment (FD) (Pearl, 2000) andothers, are not scalable due to the high computational cost of summing over a highdimensional set of variables.