Our brains are wired to constantly ask "What if?" and "Why?", which takes us beyond just raw data and into our imaginations, explanations, and actions.Through our constant curiosity, we can understand the reasons behind things, and even attempt to change or influence them.

Why Humans Think Causally

Asking "What if I had done X instead of Y?" helps us learn, plan, and assign responsibility.

Counterfactuals Are the Heart of Understanding

Don't be fooled by data! 📊🚫 Just because something happened, doesn't mean it caused it. Use reasoning to understand beyond what is seen.- Data can't predict with certainty what would have happened 🤔- Keep asking why something happened, until you understand the underlying causes 🔍

The Myth of Data-Driven Everything

When we see things happening together, it's not always enough to understand why. To truly understand, we need to ask what causes what.Sometimes, things can seem related when they actually aren't. Just because two things are happening at the same time, doesn't mean one causes the other—it could just be a coincidence! Remember to always ask "why" and look for the real cause.

Correlation Is Not Causation

Visual models like Directed Acyclic Graphs (DAGs) can help us understand links between factors and minimize biases, such as confounding variables.They show us how one event or exposure leads to another and happen before it.

Causal Diagrams Matter

To understand the effects of taking action, it's important to distinguish between observation and intervention. This process, known as causal inference, is more than just analyzing data.Key concepts:<ul><li>Intervention</li><li>Causal inference</li><li>Data mining</li></ul>

Interventions Change the Game

Pearl developed a ladder with 3 steps:Seeing - finding connections between eventsDoing - taking action to affect outcomesImagining - exploring alternative scenariosTo be truly intelligent, one must climb this ladder. 🧠

The Ladder of Causation

Statistics reveal "what is," but that isn't always enough. Understanding "what could be" and "what would have happened" requires causal reasoning.<ul><li>Statistics show us the current state of things.</li><li>But to fully understand a situation, we need to consider other possibilities.</li><li>That's where causal reasoning comes in.</li></ul>Using causal reasoning, we can explore the potential outcomes and consequences of different scenarios, providing a more complete understanding of a situation.

Statistics Alone Can’t Answer ‘Why’

To fully comprehend the world, solve issues, or make choices, we need to concentrate on "why" things occur, not just on data. Understanding "why" gives us the insight to make better decisions.Why should we focus on "why"?<ul><li>Allows for a deeper understanding of an issue or problem</li><li>Provides insight for better decision-making</li><li>Gives context to the data and the "what" in a situation</li><li>Leads to more effective problem-solving</li></ul>🤔🔍🚀

Why Matters More Than What

Judea Pearl asserts that simply gathering data is not enough to grasp causality. Instead, we need to create causal models that incorporate assumptions and establish relationships between variables.Through causal modeling, we can identify key insights and patterns to better understand cause and effect. This allows us to answer questions such as "When A happens, what can we expect for B?" and make informed decisions based on data-driven evidence.

You Can Learn Causality from Data—With a Model 🎓

"The Book of Why" explores how humans think about cause and effect. Judea Pearl revolutionizes science with causal reasoning, explaining how we can go beyond data correlation to ask deeper “why” questions—and build machines that truly understand.

A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

The Myth of Data-Driven Everything

Similar ideas to The Myth of Data-Driven Everything

Couple fights: Getting out of the habit