  A fundamental challenge of recommendation systems (RS) is understanding the
causal dynamics underlying users' decision making. Most existing literature
addresses this problem by using causal structures inferred from domain
knowledge. However, there are numerous phenomenons where domain knowledge is
insufficient, and the causal mechanisms must be learnt from the feedback data.
Discovering the causal mechanism from RS feedback data is both novel and
challenging, since RS itself is a source of intervention that can influence
both the users' exposure and their willingness to interact. Also for this
reason, most existing solutions become inappropriate since they require data
collected free from any RS. In this paper, we first formulate the underlying
causal mechanism as a causal structural model and describe a general causal
structure learning framework grounded in the real-world working mechanism of
RS. The essence of our approach is to acknowledge the unknown nature of RS
intervention. We then derive the learning objective from our framework and
propose an augmented Lagrangian solver for efficient optimization. We conduct
both simulation and real-world experiments to demonstrate how our approach
compares favorably to existing solutions, together with the empirical analysis
from sensitivity and ablation studies.
