  The goal of algorithmic recourse is to reverse unfavorable decisions (e.g.,
from loan denial to approval) under automated decision making by suggesting
actionable feature changes (e.g., reduce the number of credit cards). To
generate low-cost recourse the majority of methods work under the assumption
that the features are independently manipulable (IMF). To address the feature
dependency issue the recourse problem is usually studied through the causal
recourse paradigm. However, it is well known that strong assumptions, as
encoded in causal models and structural equations, hinder the applicability of
these methods in complex domains where causal dependency structures are
ambiguous. In this work, we develop \texttt{DEAR} (DisEntangling Algorithmic
Recourse), a novel and practical recourse framework that bridges the gap
between the IMF and the strong causal assumptions. \texttt{DEAR} generates
recourses by disentangling the latent representation of co-varying features
from a subset of promising recourse features to capture the main practical
recourse desiderata. Our experiments on real-world data corroborate our
theoretically motivated recourse model and highlight our framework's ability to
provide reliable, low-cost recourse in the presence of feature dependencies.
