EDITOR'S NOTES—COUNTERFACTUAL THINKING: A NECESSITY FOR PRACTITIONERS AND RESEARCHERS

“There are things we do not know, and things we do not know we do not know. And sometimes things we do know that are just not so” (Kay & King, 2020, p. 57).

Counterfactuals

“Of all the possible worlds… which is the best” (Simon, 2019, p. 121)? This quote sums up the concept behind counterfactuals. A counterfactual provides alternative options to consider. Counterfactuals provide more than just choices; they also offer potential outcomes for each selection. They identify “what could be or what could be made to happen” (Turner et al., 2022, p. 8).

One counterfactual definition involves imagined alternatives: “An imagined alternative to reality about the past, sometimes expressed as ‘if only…' ” (Byrne, 2016, p. 136). Another definition identifies counterfactuals as conditional statements: “Counterfactuals are conditional statements that probe the direction and stability of a relationship between an event and its consequences” (Durand & Vaara, 2009, pp. 1249–1250). Using counterfactuals aids leaders and practitioners in making better decisions in advance, rather than relying on retrospective analyses.

We typically practice counterfactual thinking whenever we try to determine “if only” or “what if” counter solutions to everyday problems. Counterfactuals can provide the following benefits for leaders, practitioners, and researchers:

• Counterfactuals justify, defend, and excuse the past.

• Counterfactual explanations require more cognitive effort than causal ones.

• Some counterfactuals explain the past, whereas others help people prepare for the future.

• Counterfactuals help to prepare for the future by influencing decisions. (Byrne, 2016, pp. 136–139)

The field of physics applied counterfactuals in constructor theory, derived by Deutsch (2013), to provide a “wider class of explanations” (p. 4335). Constructor theory compares possible states regarding explanations by asking not “what is the ‘actual' but about what could or could not be” (Marletto, 2021, p. xv).

Decision-making

The decision-making process calls for us to consider, weigh, and evaluate the best potential outcome from each alternative option. Decision-makers should “list possible courses of action, define the consequences of the various alternatives, and evaluate these consequences” (Kay & King, 2020, p. 41). Once these steps are taken, decision-makers can select the best option. Unfortunately, identifying alternative courses of action and evaluating potential consequences are often skipped. Instead, decision-makers base their decisions more on their own experiences than on selecting the best choice from several alternative options.

Research provides evidence that most people base their decisions on only considering one or two options. The option selected is typically the one most familiar to the decision maker. This approach might be acceptable for routine decisions, for those that are the same or similar to previous decisions made by the decision maker. This option is generally adequate for simple decisions or for experts with expertise and experience related to the type of decision being considered. Experts are more likely to generate counterfactuals as part of the process than novices or less experienced agents. Klein (1999) highlighted that less experienced decision-makers could not identify the sequence of events from a decision's outcome to the current time and situation. They could not work backward with appropriate data and information to support significant transitions that must take place to get from time zero (t₀) to the final desired outcome (t_n). Novices “have difficulty imagining a world different from the one they are seeing” (Klein, 1999, p. 158).

Approaches in Research

Some methods have been identified to help generate counterfactuals for researchers: counterfactual history and causal modeling.

Counterfactual history is one method that concentrates on resolving conflicts between how things have been reported compared to alternative perspectives of the same sequence of events. This approach appeals to qualitative methods in which Durand and Vaara (2009) provided a few guidelines for conducting counterfactual historical case study research:

1. Identify critical events.

2. Specify causal processes and mechanisms.

3. Use counterfactuals to establish causation. (pp. 1252–1253)

Causal modeling involves developing causal models based on supported statistical causal relationships. Some standard methods include moderation models, mediation analyses, structural equation modeling, and some Bayesian techniques. Durand and Vaara (2009) stated that causal modeling requires three essential steps:

1. Develop predictions.

2. Counterfactual reasoning.

3. Causal effect estimation.

Conclusion

Counterfactuals aid in providing a better explanation of events by identifying possible futures and alternative chains of possibilities for moving forward. It is critical to understand that counterfactuals are not based on hunches or intuition; they must be “based on sound theoretical arguments and careful empirical analyses” (Durand & Vaara, 2009, p. 1261).

Leaders and practitioners, especially those considered experts, should be skilled in generating counterfactuals in their daily work. Counterfactuals should be expressible as “statements about which transformations are possible and which are not, and why” (Marletto, 2021, p. 210). Decisions and actions will be based on incomplete data without considering counterfactuals and collecting requisite information about events or issues.

Byrne (2016) summarized counterfactuals as aiding our explanation of past events, preparing for the future, and supporting moral judgments. As a performance improvement practitioner, generating counterfactuals is necessary to master and practice. Researchers need to apply counterfactual methods in their research to consider and test alternative models based on theory and empirical evidence, to test and challenge theory, and to provide alternative inferences from research findings.

“What matters is anticipating the future rather than analysing the past” (Kay & King, 2020, p. 60).

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