The hottest Causality Substack posts right now

Behavioural polygenic scores (PGS) can show how genetic variation affects important life outcomes. It's key that these effects are meaningful for understanding social issues.
Understanding causal relationships is essential when using PGS. We need to know if the genes influence outcomes or just correlate with them.
Successful use of PGS requires good research design. Researchers should be clear about what they're measuring and ensure that genetic factors are compared to other variables correctly.

Statistical controls aren’t a magic solution; using them incorrectly can lead to wrong conclusions. It's important to understand the underlying relationships between variables before just plugging numbers into an equation.
Matching groups in studies to control for variables often isn't enough. You might still end up with biases if the controls aren’t comprehensive or well-measured.
Over-controlling or trying to account for too many factors can confuse the results. Sometimes, less control can provide a clearer picture, just like how comparing fast food and fine dining should keep their unique qualities intact.

The book discusses the justification and strengths of using machine learning in science, emphasizing prediction and adaptation to data
Machine learning lacks inherent transparency and causal understanding, but tools like interpretability and causality modeling can enhance its utility in research
The book is released chapter by chapter for free online, covering topics such as domain knowledge, interpretability, and causality

Text-only models are powerful but incomplete because language misses how the world actually looks, moves, and feels; video offers a far richer, high-volume source of physics, sound, and human behavior.
True world models must be causal and action-conditioned, predicting the next state step-by-step under intervention; autoregressive diffusion transformer architectures trained on multimodal video and actions are a promising path.
General world models will turn naive software into systems that understand and interact with the real world, enabling adaptive robots, immersive simulations, new learning tools, and large-scale scientific discovery.

Understanding is important, not just getting the right answers. If we want to make sense of our world and society, we need deeper insights and not just calculations.
Ideas about causality and certainty have changed over time. What was once thought to be clear cut, like Newton's laws, has become more complicated with concepts like quantum mechanics.
Political philosophy can be impacted by scientific ideas. If we believe everything is contradictory, it may affect how we think about power and society, sometimes leading to a focus on who holds power rather than what is right or wrong.

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Quantum mechanics was influenced by ideas that questioned the existence of an objective reality. Some scientists believed that what we observe is just a statistical illusion rather than a definite state.
Key figures like Heisenberg and Bohr argued against the law of causality, suggesting that atomic processes might not follow traditional cause-and-effect rules. They felt that understanding these processes could lead to contradictions.
Attempts to create a causal theory in quantum mechanics faced strong opposition. Despite efforts from scientists like Schrödinger, the non-causal view became widely accepted due to its statistical predictions.

The uncertainty mindset means understanding that the future is unpredictable. It allows a person to remain open to new information and possibilities.
Mindsets influence how we think and act. A flexible mindset helps us adapt to changes and can lead to new opportunities for growth.
Leaders can encourage an uncertainty mindset in their teams. This involves being honest about uncertainty and helping employees feel comfortable navigating it.

Thought needs to have some effect on the world to be useful. If it doesn't change anything, we can't test or notice it.
Actions often require more energy than the intention behind them. Like how a tiny joystick can move a heavy crane, we need stored energy to make big movements happen.
The energy involved in our actions connects back through history. Every time we move, we are using energy that has been stored and transformed, showing how life's history influences what we do.