Introduction
Summary: Causality in business means understanding how to connect the things we do with the value we create. A cause is something that makes a difference (Dave Lewis, Journal of Philosophy, 1973). If we are interested in what makes a difference in creating business value (what makes a difference in moving the truck above), we care about causality. Causal inference in business helps us create value by providing knowledge about what makes a difference so we can move resources from a lower valued use (having folks on the back of the truck) to a higher valued use (putting folks behind the truck).
We might hear the phrase correlation is not causation so often that it could easily be dismissed as a cliche, as opposed to a powerful mantra for improving knowledge and decision making. These distinctions have an important meaning in business and applied settings. We could think of businesses as collections of decisions and processes that move and transform resources. Business value is created by moving resources from lower to higher valued uses. Knowledge is the most important resource in a firm and the essence of organizational capability, innovation, value creation, and competitive advantage. Causal knowledge is no exception. Part 1 of this series discusses the knowledge problem and decisions.
In business talk can be cheap. With lots of data anyone can tell a story to support any decision they want to make. But good decision science requires more than just having data and a good story, it's about having evidence to support decisions so we can learn faster and fail smarter. In the diagram above this means being able to identify a resource allocation that helps us push the truck forward (getting people behind the truck). Confusing correlation with causation might lead us to believe value is a matter of changing shirt colors vs. moving people. We don't want to be weeks, months, or years down the road only to realize that other things are driving outcomes, not the thing we've been investing in. By that time, our competition is too far ahead for us to ever to catch up and it may be too late for us to make up for the losses of misspent resources. This is why in business, we want to invest in causes, not correlations. We are ultimately going to learn either way, the question is about if we'd rather do it faster and methodically, or slower and precariously.
How does this work? You might look at the diagram above and tell yourself - it's common sense where you need to stand to push the truck to move it forward - I don't need any complicated analysis or complex theories to tell me that. That's true for a simple scenario like that and likely so for many day to day operational decisions. Sometimes common sense or subject matter expertise can provide us with sufficient causal knowledge to know what actions to take. But when it comes to informing the tactical implementation of strategy (discussed in part 3 of this series) we can't always make that assumption. In complex business environments with high causal density (where the number of things influencing outcomes is numerous), we usually don't know enough about the nature and causes of human behavior, decisions, and causal paths from actions to outcomes to account for them well enough to know - what should I do? What creates value? In complicated business environments intuition alone may not be enough - as I discuss in part 2 of this series we can be easily fooled by our own biases and biases in the data and the many stories that it could tell.
From his experience with Microsoft, Ron Kohavi shares, up to 2/3 of the ideas we might test in a business environment turn out to either have flat results or harm the metric we are trying to improve. In Noise: A Flaw in Human Judgement authors share how often experts disagree with each other and even themselves at different times because of biases in judgement and decision making. As Stephen Wendel says you can't just wing it with bar charts and graphs when you need to know what makes a difference.
In application, experimentation and casual inference represents a way of thinking that requires careful consideration of the business problem and all the ways that our data can fool us; separating signal from noise (statistical inference) and making the connection between actions and outcomes (causal inference). Experimentation and causal inference leverages good decision science that brings together theory and subject matter expertise with data so we can make better informed business decisions in the face of our own biases and the biases in data. In the series of posts that follow, I overview in more detail the ways that experimentation and causal inference help us do these things in complex business environments.
The Value of Experimentation and Causal Inference in Complex Business Environments:
