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If you've been collecting data on yourself, it's now moderately easy to let an LLM write code that'll tease out causal relationships between those variables, e.g. with a time series causal inference library like tigramite.
I've done that with some of my data, and surprisingly it both fails to back out results from RCTs I've run, and also doesn't find many (to me) interesting causal relationships.
The most surprising one is that creatine influences creativity. Meditating more doesn't influence anything (e.g. has no influence on mood or productivity), as far as could be determined. More mindful meditations cause happiness.
Because of the resampling/interpolation necessary, everything has a large autocorrelation.
Mainly posting this here so that people know it's now easy to do this, and shill tigramite. I hope I can figure out a way of dealing with rare interventions (e.g. MDMA, CBD, ketamine), and incorporate more collected data. Code here, written by Claude 3.5.1 Sonnet.
As for data collection, I'm probably currently less efficient than I could be. The bets guide on how to collect data is imho Passive measures for lazy self-experimenters (troof, 2022), I'd add that wearables like FitBit allow for data exporting (thanks GDPR!). I've written a bit about how I collect data here, which involves a haphazard combination of dmenu pop-ups, smartphone apps, manually invoked scripts and spreadsheets converted to CSV.
I've tried to err on the side of things that can automatically be collected, for anything that needs to be manually en... (read more)