  Kalisch and B\"{u}hlmann (2007) showed that for linear Gaussian models, under
the Causal Markov Assumption, the Strong Causal Faithfulness Assumption, and
the assumption of causal sufficiency, the PC algorithm is a uniformly
consistent estimator of the Markov Equivalence Class of the true causal DAG for
linear Gaussian models; it follows from this that for the identifiable causal
effects in the Markov Equivalence Class, there are uniformly consistent
estimators of causal effects as well. The $k$-Triangle-Faithfulness Assumption
is a strictly weaker assumption that avoids some implausible implications of
the Strong Causal Faithfulness Assumption and also allows for uniformly
consistent estimates of Markov Equivalence Classes (in a weakened sense), and
of identifiable causal effects. However, both of these assumptions are
restricted to linear Gaussian models. We propose the Generalized $k$-Triangle
Faithfulness, which can be applied to any smooth distribution. In addition,
under the Generalized $k$-Triangle Faithfulness Assumption, we describe the
Edge Estimation Algorithm that provides uniformly consistent estimates of
causal effects in some cases (and otherwise outputs "can't tell"), and the
\textit{Very Conservative }$SGS$ Algorithm that (in a slightly weaker sense) is
a uniformly consistent estimator of the Markov equivalence class of the true
DAG.
