WiFi-based indoor localization solutions are actively in commercial use today. WiFi radio maps are typically created in an offline process, and location estimation is performed in realtime (online) using the maps. Both the radio map and the signal strengths provided as input during the online phase are affected by several dynamic factors resident in the environment. We call these 'causality factors'. Hence, the online location accuracy is far from the quality achieved in laboratory tests with training data, since the causality factors have varied in between. This impacts the quality expected by heterogeneous applications in real deployments. To address this issue, we investigate a novel online dynamic calibration methodology called KARMA. KARMA utilizes a one-time fingerprint of the space and systematically applies a set of causality calibration functions in real-time to compensate for the change in the factors, at test time. As a result, location providers can now significantly reduce the costly re-learning of the models for different factors, and improve real-time location prediction accuracy. Experimental studies demonstrate that KARMA's strategy, while keeping the fingerprinting task contained, can improve localization quality by a factor of 2x, compared to a typical one-state fingerprinting approach employed in many commercial deployments today. It also compares very favorably with an exhaustive all-state fingerprinting approach.