CascadiaTools worked example: Causal effects from observational data (DAG + g-computation)
Module: causal

These data are SIMULATED, so the true answer is known. See README_data.md.

To try it:
  1. Unzip this folder somewhere you can find it.
  2. Open the folder in Claude Code (with the CascadiaTools plugin installed).
  3. Paste the prompt below.

Prompt:
I surveyed 140 stream reaches last summer, one electrofishing pass each. The file is stream_trout_reaches.csv. I want to know whether riparian canopy cover actually causes more juvenile trout, and by how much for a 10 % increase in canopy, not just whether they're correlated. Elevation, farmland in the catchment and stream width all affect both canopy and trout. I think shade works partly by keeping the water cool. I also measured periphyton, which depends on both shade and how many trout graze. Please do a proper causal analysis and give me a report with figures I can use in a paper.

The analysis settings used for the published example are in config.yaml.
WALKTHROUGH.md shows every step the agent took and the results.
Website: https://cascadiawildlifelab.org/tools/modules/causal.html
