Counterfactual Reasoning in AI
What is Counterfactual Reasoning in AI?
Counterfactual reasoning in AI is a method where artificial intelligence analyzes “what-if” scenarios to predict how changing one variable could affect an outcome. By exploring alternative possibilities based on historical data, it helps AI make decisions, explain predictions, detect biases, and improve transparency, personalization, and safety in applications ranging from finance to self-driving cars.
The Basic Idea
Imagine rushing to your gate at the airport, only to arrive just a few minutes after it's closed. Frustrated that you’ve missed your flight, you may stand there for a moment thinking:
“If I’d woken up ten minutes earlier, would I still have missed my flight?”
“If I’d skipped breakfast, I bet I’d be on that plane right now.”
“Would I still have missed my flight if I had taken a taxi instead of public transit?”
“If security had been faster, I would have made it on time.”
Those thoughts are your brain conducting counterfactual reasoning: asking and answering “what if” questions. You are imagining alternative scenarios by changing just one detail of what actually happened, and reasoning if it would have led to a different outcome. You’re exploring the causal relationships between variables to judge how much they affected your gate arrival time.
Thanks to recent advancements in machine learning and artificial intelligence (AI), computers are now also able to conduct counterfactual reasoning for some scenarios. Counterfactual reasoning in AI involves estimating the potential outcomes if different decisions or actions were taken. It can help brands try to find causal relationships, like understanding if their recent marketing campaign is responsible for increased sales. It can help healthcare workers determine the best treatment plans by predicting how a change in medication or action will affect the outcome, or explore rare or dangerous situations for self-driving cars to evaluate how the technology would perform in these circumstances. The ability of AI to apply counterfactual reasoning takes it another step closer to mimicking human intelligence.1
“Counterfactual explanations are essential in bridging the gap between AI decision-making and human understanding, offering clear insights into how small changes in inputs could lead to different outcomes. This approach increases transparency, builds trust, and supports ethical AI practices."
— Bobby Zarkov, partner in financial services for KPMG Switzerland.2
About the Author
Emilie Rose Jones
Emilie currently works in Marketing & Communications for a non-profit organization based in Toronto, Ontario. She completed her Masters of English Literature at UBC in 2021, where she focused on Indigenous and Canadian Literature. Emilie has a passion for writing and behavioural psychology and is always looking for opportunities to make knowledge more accessible.
