A Practical Data Repository for Causal Learning with Big Data

Nevertheless, these expectations have met with multi-faceted obstacles. One major obstacle is ML aims to predict future observations given real-world data dependencies while human-level intelligence AI is often beyond prediction and seeks the underlying causal mechanism.

Another major obstacle is that the availability of large-scale datasets has significantly influenced causal study in various disciplines. It is crucial to leverage effective ML techniques to advance causal learning with big data. Existing benchmark datasets for causal inference have limited use as they are too "ideal", i.e., small, clean, homogeneous, low-dimensional, to describe real-world scenarios where data is often large, noisy, heterogeneous and high-dimensional.