The hottest Causation Substack posts right now

Paradoxes like Zeno’s and thought experiments like Hilbert’s Hotel don’t show that actual infinities are impossible, since infinite completed processes can be coherent and the strange results are arguably acceptable.
The Big Bang doesn’t force a beginning of time because cyclic or other models allow an infinite past, and positing a timeless origin is unsatisfying and unexplained; appeals to God or other causes fail because causation and action presuppose time.
There’s a symmetry between past and future: it’s odd to deny a possible end of time but accept a beginning, and that intuition plus the lack of any good explanation for a beginning makes an infinite past seem more plausible.

Schizophrenia is likely mostly genetic but may not be described as a genetic disease based on heritability estimates.
Genes play a crucial role in schizophrenia, serving as a risk factor and potentially a cause for the condition.
Despite the complex interplay of genetic and environmental factors, schizophrenia can be colloquially referred to as genetic due to the significant genetic contribution in its development.

Causal loops, infinite chains, and finite ones can be difficult for our minds to understand. It raises questions about how we can draw conclusions about external reality.
Time travel movies often don't make sense when characters travel back to their own universe's past. This can lead to logical inconsistencies and confusion for viewers.
The post discusses the concept of consistent time travel and the effects of actions happening before they are actually taken, raising intriguing points about the nature of time and causality.

Causal realism suggests that what is real isn't just about facts but about how ideas and beliefs can have real impacts. This means both true and false information can shape our actions and the world around us.
Drawing lines between what's real and not, or true and false, has become tricky in today's vast, information-rich world. It's important to recognize these lines can shift based on personal beliefs and cultural contexts.
We need to be cautious about simplistically labeling things as real or fake. Both can influence people's thoughts and behaviors, so acknowledging the complexity of information is crucial for understanding our reality.

Be cautious of over-extrapolating findings from laboratory experiments to real-world situations
Research in social psychology, especially in politically charged topics like race, may lack replicability and generalizability
Accuracy in summarizing and citing research in public discussions and writings is important for maintaining credibility

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Supervised machine learning helps us understand how inputs relate to outputs, but just because two things move together doesn't mean one causes the other.
To prove something causes another, experiments are the best way, but we can also make educated guesses using causal diagrams, like trees that show how different factors connect.
Machine learning models are great at predictions but aren't designed to show cause and effect; we can use them to help create clearer models for understanding these relationships.