What is the primary goal of causal inference in data analytics?

The primary goal of causal inference in data analytics is to determine if a relationship is causal or merely correlational. This involves analyzing data to establish whether changes in one variable directly cause changes in another variable, rather than just showing that they move together without a direct linkage. Establishing causal relationships is crucial for making informed decisions based on data, as it allows analysts to understand the mechanisms behind observed patterns and to assess the effectiveness of interventions or treatments.

While identifying relationships between variables and predicting future outcomes are important aspects of data analysis, they do not exclusively address the distinction between correlation and causation. Enhancing data visualization techniques is more related to how information is presented rather than how relationships are interpreted. Therefore, focusing on causal inference is essential for understanding the true dynamics between variables, which can provide deeper insights and more reliable information for decision-making.