Conferring causality on correlation

My post Cod-Science masquerading as the real thing? argued that the frequency with which management fads appear reflects the tendency of management research to veer towards pseudo-science. In it I also hypothesized that this was due to demand, supply and dissemination factors, all of which were linked– the demand for scientific rigour by management practitioners is low, therefore it is not prized by the channels of dissemination and so adherence by authors is low. Following on from that, this is the first of three articles describing why greater scepticism is required when reading confident assertions such as “my research shows…”

Anyone who has had a course in statistics will have been taught that correlation does not infer causality. But that is the first trap that those seeking to justify pet theories fall into. The pressure to deliver something perceived as useful creates a bias towards determinism – whether the relationship justifies such a stance or not – and the distillation of the real world into a model which states, if only implicitly, that if you do this, then you will get that. The logic is unashamedly deductive rather than inductive or adductive.

By definition, such models simplify; and as the experience of the banks during the financial crisis showed, even the most complex and sophisticated models can be damaging over-simplifications. Ultimately this is because any model, by definition, is assumptive. And its value is a function of the validity of the assumptions it is making, both about the variables individually and the relationships between them.

But at least the models used by banks were informed by a mass of data points – changes in asset prices collected over years and years. Such data richness is completely unavailable for management researchers for the frameworks they develop. Many of the factors that influence corporate success cannot be accurately measured – there is a need to draw conclusions from information that is often subjective and always incomplete. But that doesn’t stop the application of deductive thought processes to situations where inductive is more suited and the reduction of relationships to cause and effect. The resulting conclusions are falsely definitive –seeing causality where there is only correlation (i.e. both effects of an unidentified cause) or ignoring the possibility of reversed causality to that posited by the theory developed to explain the relationship. Most importantly, they ignore the role played by luck.

Misunderstanding causality has profound implications in both economic and business policy when models have been built on a supposed statistical relationship. The most famous example of where such a relationship proved fallacious was identified by Charles Goodhart, an economist at the Bank of England. He noted how attempts by the government to control inflation by relying on its statistical relationship with a particular measurement of the money supply (such as M3) were thwarted. As soon as the particular measure of money supply was targeted, the relationship fell apart. This observation has become known as Goodhart’s Law: “when a measure becomes a target, it ceases to be a good measure”.

In business an analogous example is the relationship between profitability and market share which has influenced strategic thinking since the 1970s. Scientific justification for this relationship was provided by Robert Buzzell whose original research showed that companies with higher relative market share (market share divided by that of the largest other company) achieved superior profitability; the theory for why this should be the case was provided by the experience curve and the Boston Consulting Group’s growth-share matrix (where the x-axis was a logarithmic scale of relative market share) providing the means for practical application.

The growth-share matrix was originally designed to assist the process of resource allocation across business units in the group portfolio, to make sure corporate funds flowed to flow to where they would generate the best returns. But even if it was not designed to shape the strategies of the individual businesses within the portfolio by mandating objectives, the implication of such thinking was clear – businesses should seek to maximise market share relative to competitors to ensure their costs declined with experience at a faster rate than rivals and thereby enjoy an advantaged cost position. Add in managerial self-interest – the prescription for businesses that were not market share leaders or in high growth markets (by definition, the majority of businesses unless you take a very generous view on market segmentation) was closure or divestiture – and it is easy to see why growing market share became seen as a strategic necessity. But the process of achieving a high relative market share often inverted the purported relationship – the headlong dash for share fuelled by price cuts that resulted in an interminable price war, margin erosion and a much weakened company.

This is not to argue that the experience curve doesn’t exist merely that basing strategy around it is dangerous – especially in mature,

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low growth industries where significant efficiency gains had already been rung out. Also cost reductions are not an automatic prize of self-enlargement – an assumption that permeates the original thinking (indeed it has set a precedent that strategists just need to identify opportunities and place those in operations on the hook to deliver them) – and need to be targeted and delivered through negotiation with suppliers, an ethos of continuous improvement and re-configuration of work practices. Also, as Richard Miniter mischievously points out in The Myth of Market Share, the underlying relationship could just as equally be inverted – profit leaders achieve market leadership because their superior returns enable them to re-invest at a

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faster rate than their rivals.

Similar assumptions about cause and effect are widespread in management literature – namely that superior financial performance can be definitively traced to key decisions or actions and the key to great performance is studying greatness. This assumed relationship is one of the foundations of traditional management research. One consequence is that the same companies are trotted out again and again as exemplars of range of different practices. This was first brought home to me several years ago when two articles in the April 2004 edition of the Harvard Business Review cited the same three companies (Toyota, Dell and Wal-Mart) to trumpet the success derived from hardball strategy and operational innovation respectively.

Successful companies have multiple practices that are good. As such it is easy to take a successful company and project onto it pet theories, then interview people who will support it to provide a double dose of confirmation bias. But that still leaves the small problem of selecting which factors have had the greatest impact and those for which the causality between profitability and capability is reversed – success and greater profitability has enabled higher levels of investment in certain areas, rather than vice versa.

Such studies all require historical review, this introduces a whole range of possibilities for flawed logic – fallacies of causation being just one category. These historian’s fallacies will be the subject of my next blog post.

Elusive Growth: Why prevailing practices in strategy, marketing and management education are the problem, not the solution, by Jack Springman, will be published in Summer 2011

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About Jack Springman

I am a consultant with experience in business strategy and customer strategy development, customer management and customer service transformation.