Teaching counterfactual reasoning in economics education
A crucial EA concept for high school economics is counterfactual reasoning – systematically asking “what would have happened if agent X had not done action Y?” This is essential for understanding the actual impact of interventions.
Why it matters:
Many interventions don’t create as much value as they appear because something similar would have happened anyway
The true impact is only the additional change caused by the intervention – the difference between what actually happened and what would have happened in the counterfactual scenario
It’s counterintuitive – our brains naturally credit actions without considering what would have occurred otherwise
Methods to evaluate counterfactual impact:
Randomized controlled trials (RCTs): Randomly assign some groups to receive an intervention and others not, then compare outcomes. The control group approximates what would have happened without the intervention.
Before-and-after with comparison groups: Compare changes in a treated group to changes in a similar untreated group over the same period. This helps account for broader trends that would have occurred anyway.
Trend analysis: Plot pre-intervention trends and project them forward. If post-intervention outcomes match the projected trend, the intervention may have had little counterfactual impact.
Natural experiments: Find situations where an intervention occurred in one place but not another similar place due to arbitrary reasons, allowing comparison.
Classroom applications:
Analyze case studies using these methods (e.g., evaluating a job training program’s effectiveness)
Have students design simple evaluation plans for school or community interventions
Critique news articles that claim causation without proper counterfactual analysis
This teaches students both to think counterfactually and to evaluate causal claims empirically.
(Comment made in collaboration with generative AI)
Teaching counterfactual reasoning in economics education
A crucial EA concept for high school economics is counterfactual reasoning – systematically asking “what would have happened if agent X had not done action Y?” This is essential for understanding the actual impact of interventions.
Why it matters:
Many interventions don’t create as much value as they appear because something similar would have happened anyway
The true impact is only the additional change caused by the intervention – the difference between what actually happened and what would have happened in the counterfactual scenario
It’s counterintuitive – our brains naturally credit actions without considering what would have occurred otherwise
Methods to evaluate counterfactual impact:
Randomized controlled trials (RCTs): Randomly assign some groups to receive an intervention and others not, then compare outcomes. The control group approximates what would have happened without the intervention.
Before-and-after with comparison groups: Compare changes in a treated group to changes in a similar untreated group over the same period. This helps account for broader trends that would have occurred anyway.
Trend analysis: Plot pre-intervention trends and project them forward. If post-intervention outcomes match the projected trend, the intervention may have had little counterfactual impact.
Natural experiments: Find situations where an intervention occurred in one place but not another similar place due to arbitrary reasons, allowing comparison.
Classroom applications:
Analyze case studies using these methods (e.g., evaluating a job training program’s effectiveness)
Have students design simple evaluation plans for school or community interventions
Critique news articles that claim causation without proper counterfactual analysis
This teaches students both to think counterfactually and to evaluate causal claims empirically.
(Comment made in collaboration with generative AI)