  We consider the problem of constructing diffusion operators high dimensional
data $X$ to address counterfactual functions $F$, such as individualized
treatment effectiveness. We propose and construct a new diffusion metric $K_F$
that captures both the local geometry of $X$ and the directions of variance of
$F$. The resulting diffusion metric is then used to define a localized
filtration of $F$ and answer counterfactual questions pointwise, particularly
in situations such as drug trials where an individual patient's outcomes cannot
be studied long term both taking and not taking a medication. We validate the
model on synthetic and real world clinical trials, and create individualized
notions of benefit from treatment.
