What is Causal Analysis?

An important detail to a lot of the analyses we’ve looked at so far is that we can only see correlations and can’t get at the cause of the relationships we observe. Causal analysis fills that gap; the goal of causal analysis is to see what happens to one variable when we manipulate another variable - looking at the cause and effect of a relationship. Generally, causal analyses are fairly complicated to do with observed data alone; there will always be questions as to whether it is correlation driving your conclusions or that the assumptions underlying your analysis are valid. More often, causal analyses are applied to the results of randomized studies that were designed to identify causation. Causal analysis is often considered the gold standard in data analysis, and is seen frequently in scientific studies where scientists are trying to identify the cause of a phenomenon, but often getting appropriate data for doing a causal analysis is a challenge. One thing to note about causal analysis is that the data is usually analyzed in observed relationships are usually average effects. While on average giving a certain population a drug may lessen the symptoms of a disease, this causal relationship may not hold true for every single affected individual. As said before, many scientific studies allow for causal analyses. Randomized control trials for drugs are a prime example of this. For example, one randomized control trial examined the effects of a new drug on treating infants with spinal muscular atrophy. Comparing a sample of infants receiving the drug versus a sample receiving a mock control, they measure various clinical outcomes in the babies and look at how the drug affects the outcomes.

References: https://www.coursera.org/learn/data-scientists-tools/lecture/7Jmzs/types-of-data-science-questions