  Case-control designs are an important tool in contrasting the effects of
well-defined treatments. In this paper, we reconsider classical concepts,
assumptions and principles and explore when the results of case-control studies
can be endowed a causal interpretation. Our focus is on identification of
target causal quantities, or estimands. We cover various estimands relating to
intention-to-treat or per-protocol effects for popular sampling schemes
(case-base, survivor, and risk-set sampling), each with and without matching.
Our approach may inform future research on different estimands, other
variations of the case-control design or settings with additional complexities.
