In workforce analytics just pretend that correlation is the same as causality
If you read any article, book or blog post about workforce analytics, HR data or evidence based HR you will eventually read the following sentence “…but remember that correlation is not the same as causality”. And this is factually correct. Just because two items correlate does not mean that they are connected or that there is causality.
The problem is that it does not help you with your communication with top management or your recommendations for action. Take this example;
Imagine that you have worked really hard with your analytics project, and you have build a model which shows connections between various training programs and profits. You identify that one particular training program correlate very strongly with profits. You then produce your fine PowerPoint slides and present your findings to the management board. You say “I have identified this strong correlation between this particular program and bottom line profits BUT as you all know, correlation does not mean causality”. The managers look at each other and one say “So what do you say, should we roll this program out to all of our sales people or not?”. You look confused down in your papers and then respond “Well, I can’t actually say for sure that attending this training program will make your sales people produce higher profits or if it is the sales people who are very profitable who happens to attend this particular training program for unknown reasons. The only thing I can say is that the two variables appear to correlate”. The meeting ends and you wonder why your next analytics project don’t get funded.
You see the problem? On one hand you are right. You cannot for sure say which way the arrow turns between the two variables. But on the other hand the management board does not care about correlations (nor should they), they only care about solid and value-added recommendations.
What to do? My advise is; pretend that correlation is the same as causality. Test your hypothesis with qualitative data and present a recommendation based on your findings. So in the above example, interview five sales managers and ask them about their view on the causality; do they believe that the particular training program has led to significant increase in profits from the sales people attending the program? If it has they that’s it. I know it is not ‘correct’ or ‘true’ but it is good enough. And most likely you will be right.
I know there are a lot of fancy and even simple ways to test the hypothesis with time-lagging-tests or blind-tests or something similar. I have just experience a lot of places where even that kind of tests are not possible, too expensive or not available due to poor data. Make it easy. Do a qualitative test and present your findings as if correlation is the same as causality
(just don’t tell anybody I told you so – this approach is not looked well upon in analytics circles. Leaders don’t mind – trust me)