A deep dive into associations
Traditional statistical methods primarily focus on the uncertainty in finite datasets. On the other hand, infinite data theoretically eliminates this uncertainty, allowing precise calculations of statistical measures such as means and variances. Infinite data eliminates statistical uncertainty by providing all possible observations, thus removing sampling error and variability.
However, causal inference faces unique challenges that persist regardless of data volume. Infinite data can accurately depict correlations between variables, but it does not clarify the direction or nature of these relationships. This is where causal inference becomes crucial, requiring methods and theories beyond mere pattern recognition to uncover true causal dynamics. It’s essential to grasp that statistical association and/or correlation is not synonymous with causation. Distinguishing causal relationships from statistical associations requires more than abundant data...
