Correlation Does Not Imply Causation

Jan 26, 2012

As we prepare for a new year, and as I look forward to preparing for a metrics panel at the Spring 2012 Expo, I have been pairing a series of thoughts on metrics and measures that are important to talent acquisition.

For the past several months, my team has reviewed dozens of articles, blogs, and white papers that outline foundational and basic aspects of “How to do Metrics.” There is a tremendous resource available by simply using search engines to find information on metrics.

I am encouraged by the amount of content that is dedicated to subjects such as what metrics can be tracked, the quality of hire conversation, the candidate experience, and how metrics can serve as a stepping stone to a real relationship with business leaders. I will also admit that the meat behind many of these blogs, articles, or white papers is pretty lean, but there are exceptions. Shout out to Chris Brabic at Smashfly for his tutorials that break into some of the detail.

As I prepare for the metrics panel for the spring ERE conference, it occurred to me how statistics and analysis tends to not be standard training for recruiters. There are some recruiters who were engineers, programmers, or MBAs, and as such they would have some basic to intermediate statistics training. But it is likely that statistical analysis or training is likely reinforced by using Excel with tables, pie charts and graphs — not using the actual definitions, architecture, and structure of true statistical analysis.

Which brings me to this post, and the danger of correlation and causation. It is not new to hear that metrics, when pulled together and compared to each other, tell a story. Much of that story has to do with correlation. As an example, if you spend more money (increase cost per hire), you may reduce your time to fill. Well, sometimes that is true. Sometimes.

That relationship may not be a causal relationship: One does not necessarily cause the other. The dependence that we wish was there is actually not there in the strength that we need it to be, or even at all. There is a common scientific and statistical concept that states “correlation does not imply causation.” I find that to be very true in recruiting and talent acquisition metrics.

We try so hard to find how one metric impacts the other. Technologies, branding companies, consultants, and so on use metrics to drive home value — and they should. We all try hard because we just really want to sort out why things are happening and what can we do to change what is happening, and that is a worthy endeavor.

However, I caution trying to correlate metrics together in order to force causation. It is more likely that two or more metrics correlate and have less of a causal relationship then having a causal relationship.

As you review your metrics and measures for 2012, I encourage you to:

  1. State which metrics you are correlating together, and challenge yourself to see if you are hoping for a causal relationship, or if a causal relationship actually exists.
  2. Prove that the causal relationship has validity and can be repeated time and time again.
  3. Go back to your executive presentations and record where you did indicate that correlations and causal relationships exist. Remember that those statements are now out there, and it is possibly expected that the causal relationship will sustain.
  4. As you create or refine goals for your recruiting teams or the hiring managers, be aware of these causal and non-causal correlations, as it will help you declare and meet expectations in the marketplace.

Happy metric-ing, and see you at the Spring ERE!

Get articles like this
in your inbox
Subscribe to our mailing list and get interesting articles about talent acquisition emailed weekly!