Causal Inference and Data Science: Why They Need Each Other
Introduction
In this “big data” era, data is an essential fuel offering an opportunity to rethink precisely with domain knowledge and approaches. But how can this data-centric view be achieved? Yes, possible under the umbrella of “data science”.
A comprehensive understanding of data science and its tasks is crucial to interpret insights and translate novel discoveries into practices to make informed decisions.
In particular, the tasks under data analysis can be categorized into three parts;
- Description: to focus on explaining patterns and trends in order to understand occurrences of points of interest.
- Prediction: To identify patterns and anticipate viable possibilities, and
- Causal Inference: To determine how one factor impacts another and examine how changing one factor could change another.
Understanding Causal Inference in the context of data science
Causal inference explains the cause of changes into a variable if the changes occur in a different variable where statistical methods are used to determine how modification in one variable is correlated to another variable. Causal inference relies on causal assumptions.
“Defining cause-and-effect, causal inference addresses impact issues or addresses why something is happening. Unlike description and prediction, the findings cannot be obtained only from data, it also calls for strict conditions and domain expertise.
According to a survey “With growing awareness, 83% of respondents say that causal inference will be of increasing importance for data-driven decision making in the future, while 44% say causal inference is already important in their data science projects.”
Data science helps people to make informed decisions, and the ability of data science to improve decision making is predicated on the potential use of data, and causal inference helps in making decisions by tackling “what if ” questions or analyzing benefit-risk conditions.
Therefore, coupling data science and causal inference assist in understanding the appropriate use of analytical methods to improve the relevance and utility of data.
Summing up
In the end, we can conclude that an increasing volume of data coupled with data science presents an opportunity to unlock the wealth of big data. The integration of advanced algorithms and causal inference that demands domain knowledge is crucial to understanding a complex system and how it behaves under certain identifiable circumstances.
