Recent years have witnessed an increasing interest in causal reasoning for designing fair decision-making systems due to its compatibility with legal frameworks, interpretability for human stakeholders, and robustness to spurious correlations, among other factors. The recent attention, however, has been accompanied with great skepticism due to practical and epistemological challenges with applying existing causal fairness approaches. Motivated by the long-standing empirical work on causality in econometrics, social sciences, and biomedi- cal sciences, in this paper we lay out the conditions for appropriate application of causal fairness under the “potential outcomes framework.” To this end, we propose a shift from postulating interventions on immutable social categories to their perceptions and highlight two key aspects of interven- tions that are largely overlooked in the causal fairness literature: timing and nature of manipulations. We argue that such conceptualization is key in evaluating the validity of causal assumptions and conducting sound causal analysis includ- ing avoiding post-treatment bias. Further, we illustrate how causality can address the limitations of existing fairness met- rics that depend upon statistical correlations. Specifically, we introduce causal variants of common statistical fairness no- tions, and we make a novel observation that under the causal framework there is no fundamental disagreement between different fairness criteria. Finally, extensive experiments on synthetic and real-world datasets including a case study on police stop and search decisions demonstrate the efficacy of our framework in evaluating and mitigating unfairness.