  In this paper, we adopt results in nonlinear time series analysis for causal
inference in dynamical settings.~Our motivation is policy analysis with panel
data, particularly through the use of "synthetic control" methods. These
methods regress pre-intervention outcomes of the treated unit to outcomes from
a pool of control units, and then use the fitted regression model to estimate
causal effects post-intervention. In this setting, we propose to screen out
control units that have a weak dynamical relationship to the treated unit. In
simulations, we show that this method can mitigate bias from "cherry-picking"
of control units, which is usually an important concern. We illustrate on
real-world applications, including the tobacco legislation example of
\citet{Abadie2010}, and Brexit.
