23 Sep 2025
Imagine waking up one froggy and cloudy day. On this day, you’re feeling rather groggy and slow.
You make coffee, assuming it will wake you up.
You check the weather before picking your outfit, expecting it will help you dress appropriately.
You leave home early because you believe traffic gets worse later in the morning.
At every step, you’re making causal assumptions—even if you don’t consciously think about it.
Causality is the idea that one thing leads to another: coffee increases alertness, checking the weather prevents bad outfit choices, leaving early reduces commute time. Our thinking is fundamentally causal—we operate under the assumption that our actions have predictable effects, even when we don’t have perfect data to confirm them. Human knowledge is inherently causal, built on intensely researched, tested and explainable cause-and-effect relationships. Take the famous third law of Newton:
For every action, there is an equal and opposite reaction.
Knowing this, you are able to:
- Identify the cause and effect of certain physical phenomenal -> Explainability
- Predict precisely the result of the aforementioned phenomenal -> Predictability
In essence, what we accept as knowledge is nothing more than a collection of true causal relationships—causes that reliably produce their effects. Knowledge is valuable because learning a true causal relationship is difficult and expensive, and because having knowledge helps us make (wayyyyyyyyy) better decisions, and making better decisions make difference.
Bad news: this also means most of our everyday untested thoughts - our assumptions about how the world works - are imprecise, and worst, plain wrong.
Some phenomena are easily observable through our senses, making their cause-and-effect relationships more apparent. For example, we can quickly identify the source of an unpleasant smell or realize that the cat knocked over the vase. However, there are also phenomena that are much harder to understand. These are often things we can’t directly observe with our senses but can study through data—such as the effects of a drug on patients, the impact of welfare on the economy, or the causes of a disease. For phenomena that occur in complex contexts, we need a method to develop and test hypotheses in order to build knowledge. This is the focus of many causal frameworks, and the topic of our next posts.