ARTIFICIAL INTELLIGENCE

“A counterfactual is a hypothetical scenario or statement that considers what would have happened if a specific event or condition had been different from what actually occurred. In simple terms, it is a ‘what if’ or ‘if only’ thought process that contradicts the established facts.” – Counterfactual

A counterfactual is a hypothetical scenario or statement that imagines what would have happened if a specific event, condition, or action had differed from what actually occurred. It represents a ‘what if’ or ‘if only’ thought process that directly contradicts established facts, enabling exploration of alternative possibilities for past or future events.

Counterfactual thinking involves mentally simulating outcomes contrary to reality, such as ‘If I had not taken that sip of hot coffee, I would not have burned my tongue.’ This cognitive process is common in reflection on mistakes, regrets, or opportunities, like pondering ‘If only I had caught that flight, my career might have advanced differently.’^1,2,3

Key Characteristics and Types

• Additive vs. Subtractive: Additive counterfactuals imagine adding an action (e.g., ‘If I had swerved, the accident would have been avoided’), while subtractive ones remove one (e.g., ‘If the child had not cried, I would have focused on the road’).³
• Upward vs. Downward: Upward focuses on better alternatives, often leading to regret; downward considers worse ones, fostering relief.³
• Mutable vs. Immutable: People tend to mutate exceptional or controllable events in their imaginings.¹

Applications Across Disciplines

In causal inference, counterfactuals estimate effects by comparing observed outcomes to hypothetical ones, such as ‘What would the yield be if a different treatment was applied to this plot?’ They underpin concepts like potential outcomes in statistics.^4,7

In philosophy and logic, counterfactuals are analysed as conditionals where the antecedent is false, symbolised as A ?? C (if A were the case, C would be), contrasting with material implications.⁶

In machine learning, counterfactual explanations clarify model decisions, e.g., ‘If feature X changed to value x, the prediction would shift.’²

Everyday examples include regretting a missed job (‘If I had not been late, I would have that promotion’) or entrepreneurial reflection (‘If we chose a different partner, the startup might have succeeded’).³

Leading Theorist: Judea Pearl

The most influential modern theorist linking counterfactuals to strategy is Judea Pearl, a pioneering computer scientist and philosopher whose causal inference framework revolutionised how counterfactuals inform decision-making, policy analysis, and strategic planning.

Biography: Born in 1936 in Tel Aviv, Pearl emigrated to the US in 1960 after studying electrical engineering in Israel. He earned a PhD from Rutgers University in 1965 and joined UCLA, where he is now a professor emeritus. Initially focused on AI and probabilistic reasoning, Pearl developed Bayesian networks in the 1980s, earning the Turing Award in 2011 for advancing AI through probability and causality.

Relationship to Counterfactuals: Pearl’s seminal work, Probabilistic Reasoning in Intelligent Systems (1988) and Causality (2000), formalised counterfactuals using structural causal models (SCMs). He defined the counterfactual query ‘Y would be y had X been x’ via do-interventions and potential outcomes, e.g., Y_x(u) = y denotes the value Y takes under intervention do(X=x) in unit u’s background context.⁴ This ‘ladder of causation’-from association to intervention to counterfactuals-enables strategic ‘what if’ analysis, such as evaluating policy impacts or business decisions by computing missing data: ‘Given observed E=e, what is expected Y if X differed?’⁴

Pearl’s framework aids strategists in risk assessment, A/B testing, and scenario planning, distinguishing correlation from causation. His do-calculus provides computable algorithms for counterfactuals, making them practical tools beyond mere speculation.^4,7